@Article{info:doi/10.2196/36907,
author="B{\"o}rve, Alexander",
title="From Teledermatology to Dermatology Artificial Intelligence: Will Teledermatology Exist in the Next 2 Years?",
journal="iproc",
year="2022",
month="Mar",
day="8",
volume="8",
number="1",
pages="e36907",
keywords="artificial intelligence",
keywords="teledermatology",
keywords="dermatology",
abstract="Background: Dermatology has been proven to be well suited for store-and-forward telemedicine triaging. With the reduced cost of computer power and readily available deep convolutional neural networks, using the digital images collected with store-and-forward, machine learning has made it possible to create artificial intelligence (AI) models. The AI models can analyze new digital images taken with a smartphone camera and return reliable dermatology outputs within seconds. Objective: The aim of this study is to demonstrate the shift from teledermatology to dermatology AI. Methods: A literature search was conducted and experience from a web-based teledermatology service was also considered. Results: There has been a slow uptake of teledermatology in a clinical setting and by consumers. The development of AI dermatology models has gained momentum over the last few years. Studies have shown that AI dermatology is on par with teledermatology to deliver accurate diagnoses for the most common dermatology pathologies. Conclusions: Teledermatology has still not gained mass adoption, both clinically and directly by consumers. The fast pace of AI dermatology development indicates that the technology will surpass store-and-forward teledermatology as a first point of digital consultation in dermatology. Conflicts of Interest: AB is the owner of iDoc24 Inc. ",
doi="10.2196/36907",
url="https://www.iproc.org/2022/1/e36907"
}


@Article{info:doi/10.2196/36905,
author="Conover, Susan",
title="Teledermatology and Artificial Intelligence: Piction Health",
journal="iproc",
year="2022",
month="Mar",
day="8",
volume="8",
number="1",
pages="e36905",
keywords="artificial intelligence",
keywords="machine learning",
keywords="clinical decision support",
keywords="dermatology",
keywords="AI",
abstract="Background: Skin diseases affect 2.3 billion people globally. Due to the scarcity of dermatologists, 2 in 3 cases are seen by primary care physicians (PCPs), who have lower diagnostic accuracy. Published studies have shown that the diagnostic accuracy of a PCP or general practitioner is close to 50\%. Objective: The aim of this study was to build artificial intelligence (AI) classifiers across 26 and 54 common and urgent adult rashes that present in a primary care setting. Methods: We trained our AI models with approximately 50,000 total photos. The number of images within each disease or class ranged from 76 to 5505. Additionally, we further tested narrowing the differential diagnosis by adding body part information to identify how this impacts top-5 accuracy for one condition. Results: Overall, we trained an AI model to identify 26 classes on par with the accuracy level of a dermatologist, who is, on average, 75\% top-3 accurate across 26 conditions. Additionally, we trained the AI model across 54 conditions and achieved 74.3\% top-5 accuracy across common conditions and 79.2\% top-5 accuracy across urgent conditions. In evaluating if body part information may increase top-5 accuracy, we saw top-5 accuracy for one condition increase from 67\% to 97\%. Conclusions: Overall, we concluded that including body part information to down-select possible disease matches substantially increased the overall differential diagnosis accuracy for body region--specific conditions. We also concluded that AI may assist PCPs to identify the most likely skin conditions quickly in a clinical encounter, improve overall diagnostic accuracy, and inform the most appropriate next step for the patient. These promising findings highlight the need and potential of AI and clinical decision support to augment the ability of PCPs to accurately and confidently evaluate patients with skin conditions. Conflicts of Interest: SC is the cofounder and chief executive officer of Piction Health, a company focused on using AI to help augment frontline providers' clinical decision making to save time and improve outcomes for patients with skin diseases. SC also holds shares in Piction Health. ",
doi="10.2196/36905",
url="https://www.iproc.org/2022/1/e36905"
}


@Article{info:doi/10.2196/36901,
author="McKoy, Karen",
title="A Review of International Teledermatology",
journal="iproc",
year="2022",
month="Mar",
day="3",
volume="8",
number="1",
pages="e36901",
keywords="international",
keywords="COVID-19",
keywords="dermatology",
keywords="teledermatology",
keywords="telemedicine",
abstract="Background: The use of teledermatology has been evolving slowly for the delivery of health care to remote and underserved populations. Advancements in technology and the recent COVID-19 pandemic have hastened its use internationally. Objective: An international survey was done to assess teledermatology use before and during the COVID-19 pandemic. Methods: In addition to an updated literature review from 2015 to 2021, a survey instrument was formatted in Google Forms in English and distributed electronically to international personal contacts of the authors, as well as to international dermatology and teledermatology societies of members of the International League of Dermatological Societies and members of the International Society of Teledermatology. Answers from US dermatologists were excluded. Results: 110 survey responses were received from 33 countries. Barriers to the use of teledermatology have fallen considerably in the last year. Conclusions: Teledermatology use has increased significantly in recent years in both government-sponsored and private health care systems and individual practices. There are no recognized international practice guidelines and there is variable use within countries. Many barriers remain to increasing the use of teledermatology. Conflicts of Interest: None declared. ",
doi="10.2196/36901",
url="https://www.iproc.org/2022/1/e36901"
}


@Article{info:doi/10.2196/36906,
author="Garfinkel, Jason
and Chandler, C.
and Rubinstein, G.
and Jain, Manu",
title="Confocal Microscopy and its Role in Teledermatology: Diagnosis of Basal Cell Carcinoma in a Clinical Setting",
journal="iproc",
year="2022",
month="Mar",
day="2",
volume="8",
number="1",
pages="e36906",
keywords="reflectance confocal microscopy",
keywords="teledermatology",
keywords="telemedicine",
keywords="tele-RCM",
keywords="skin cancer",
keywords="basal cell carcinoma",
abstract="Background: Reflectance confocal microscopy (RCM) is a noninvasive tool that is used to diagnose skin cancers. However, RCM requires an expert consultation, which is often performed via store-and-forward (SAF) teledermatology. Unfortunately, SAF does not mimic bedside diagnosis, nor permits interaction between the remote expert reader, physician, and patient. Recently, a live interactive method (LIM)--tele-RCM approach was shown to diagnose basal cell carcinoma (BCC) from a remote location, demonstrating advantages over SAF by providing a bedside diagnosis during consultation. Objective: The aim of this study is to validate the LIM-tele-RCM approach to diagnose BCC in a real-world setting. Methods: In this pilot study, 4 patients with 6 clinically suspicious BCC lesions were enrolled and imaged with RCM at a Los Angeles dermatology clinic. A Health Insurance Portability and Accountability Act--compliant teleconferencing application was used to livestream RCM images to an expert RCM reader in New York. The expert reader had remote control of the software, direct audio communication with the clinic, and the patient's clinical history with dermoscopy. During imaging, RCM features were noted, and a diagnosis was made at the bedside. After imaging, patients completed a short questionnaire (on a 5-point scale, with 5 being the highest score) about satisfaction, comfort, and communication during the session.? Results: RCM diagnosed 4/6 (67\%) lesions correctly as BCC and 2/6 (33\%) were false-positive diagnoses. The true-positive lesions had ``tumor islands with palisading and clefting'' and were directly managed with Mohs surgery. The false-positive lesions had ``dark silhouettes'' (a common false-positive feature for BCC) and underwent a shave biopsy for confirmation. The entire session ranged from 15 to 20 minutes (an average of 17.7 minutes), comparable to the reported RCM procedure time. On the questionnaire, all patients responded with the highest rating (5/5) for each question. Conclusions: LIM-tele-RCM demonstrates potential advantages over the SAF method, enabling bedside diagnosis with similar diagnostic accuracy as reported in the literature and proper management. Additionally, the remote reader had access to patients' clinical backgrounds and could engage with patients. It may also be useful for training novice RCM users and beneficial in settings where remote diagnostics are desired, such as during the COVID-19 pandemic. However, technical challenges such as image quality degradation during video streaming, poor internet bandwidth, and end user latency may impact diagnosis. Larger, multicenter studies are needed to assess the accuracy of LIM-tele-RCM for the diagnosis of BCC and other neoplastic and inflammatory lesions, and to quantify technical limitations. Acknowledgments: This work was funded by the National Cancer Institute Cancer Center (Grant P30 CA008748). Conflicts of Interest: None declared. ",
doi="10.2196/36906",
url="https://www.iproc.org/2022/1/e36906"
}


@Article{info:doi/10.2196/36896,
author="Conforti, Claudio",
title="Impact of the COVID-19 Pandemic on Dermatology Practice Worldwide: Results of a Survey Promoted by the International Dermoscopy Society",
journal="iproc",
year="2022",
month="Mar",
day="2",
volume="8",
number="1",
pages="e36896",
keywords="COVID-19",
keywords="pandemic",
keywords="teledermatology",
keywords="telemedicine",
abstract="Background: The International Dermoscopy Society (IDS) conducted an online survey to investigate the impact of the COVID-19 outbreak on the daily practice of dermatologists working with patients with skin cancer, to collect data regarding the frequency of skin manifestations noticed by the members, and to obtain information about the use of teledermatology during the pandemic. Objective: The aims of this study are to identify changes within dermatology departments during lockdowns, to evaluate the use of teledermatology during the COVID-19 pandemic, and to find the most frequent cutaneous manifestations associated with COVID-19. Methods: All IDS members (approximately 160,000 members) were asked to fill in a questionnaire sent by email. The questionnaire was available in English and was anonymous, with a compiling time of less than 5 minutes. The survey was open for 30 days (from April 24, 2020, to May 24, 2020) and it could only be filled out once. Results: Overall, 678 dermatologists responded to the questionnaire; of these, 334 members stated that there had been a reduction of more than 75\% in daily work activity during the pandemic, 265 dermatologists worked fewer days per week, and 118 experienced telemedicine for the first time. Acrodermatitis was the most frequently observed skin manifestation (n=80), followed by urticarial rash (n=69), morbilliform rash (n=53), and purpuric manifestation (n=40). Regarding the role of teledermatology, 565 dermatologists reported an increased number of teleconsultations, and the number of melanomas diagnosed during the pandemic was practically 0 for 385 (56.8\%) respondents. Conclusions: This survey highlights that the outbreak had a negative impact on most dermatology services, with a significant reduction in consultation time spent for patients with chronic conditions, and an increased risk of missed melanoma and nonmelanoma skin cancer diagnosis. Moreover, our study confirms earlier findings of a wide range of skin manifestations associated with COVID-19. Conflicts of Interest: None declared. ",
doi="10.2196/36896",
url="https://www.iproc.org/2022/1/e36896"
}


@Article{info:doi/10.2196/36902,
author="Felmingham, Claire
and Byars, Gabrielle
and Cumming, Simon
and Brack, Jane
and Ge, Zongyuan
and MacNamara, Samantha
and Haskett, Martin
and Wolfe, Rory
and Mar, Victoria",
title="Testing Artificial Intelligence Algorithms in the Real World: Lessons From the SMARTI Trial",
journal="iproc",
year="2022",
month="Mar",
day="1",
volume="8",
number="1",
pages="e36902",
keywords="melanoma",
keywords="artificial intelligence",
keywords="algorithm",
keywords="dermatology",
keywords="skin cancer",
abstract="Background: A number of studies have shown promising performance of artificial intelligence (AI) algorithms for diagnosis of lesions in skin cancer. To date, none of these have assessed algorithm performance in the real-world setting. Objective: The aim of this project is to evaluate practical issues of implementing a convolutional neural network developed by MoleMap Ltd and Monash University eResearch in the clinical setting. Methods: Participants were recruited from the Alfred Hospital and Skin Health Institute, Melbourne, Australia, from November 1, 2019, to May 30, 2021. Any skin lesions of concern and at least two additional lesions were imaged using a proprietary dermoscopic camera. Images were uploaded directly to the study database by the research nurse via a custom interface installed on a clinic laptop. Doctors recorded their diagnosis and management plan for each lesion in real time. A pre-post study design was used. In the preintervention period, participating doctors were blinded to AI lesion assessment. An interim safety analysis for AI accuracy was then performed. In the postintervention period, the AI algorithm classified lesions as benign, malignant, or uncertain after the doctors' initial assessment had been made. Doctors then had the opportunity to record an updated diagnosis and management plan. After discussing the AI diagnosis with the patient, a final management plan was agreed upon. Results: Participants at both sites were high risk (for example, having a history of melanoma or being transplant recipients). 743 lesions were imaged in 214 participants. In total, 28 dermatology trainees and 17 consultant dermatologists provided diagnoses and management decisions, and 3 experienced teledermatologists provided remote assessments. A dedicated research nurse was essential to oversee study processes, maintain study documents, and assist with clinical workflow. In cases where AI algorithm and consultant dermatologist diagnoses were discordant, participant anxiety was an important factor in the final agreed management plan to biopsy or not. Conclusions: Although AI algorithms are likely to be of most use in the primary care setting, higher event rates in specialist settings are important for the initial assessment of algorithm safety and accuracy. This study highlighted the importance of considering workflow issues and doctor-patient-AI interactions prior to larger-scale trials in community-based practices. Acknowledgments: This research was supported by the Victorian Medical Research Acceleration Fund, with 1:1 contribution from MoleMap Ltd. VM is supported by the National Health and Medical Research Council Early Career Fellowship. CF is supported by the Monash University Research Training Program Scholarship. Conflicts of Interest: SM is head of clinical research and regulatory affairs at Kahu.ai Ltd, a subsidiary of MoleMap Ltd. MH was the chief medical officer and a director of MoleMap Ltd, and holds shares in MoleMap Ltd. Trial Registration: ClinicalTrials.gov NCT04040114; https://clinicaltrials.gov/ct2/show/NCT04040114 ",
doi="10.2196/36902",
url="https://www.iproc.org/2022/1/e36902"
}


@Article{info:doi/10.2196/36884,
author="Muthiah, Shareen
and Morton, A. Colin",
title="Digital Dermatology: Experience From Scotland During Lockdown and Beyond",
journal="iproc",
year="2022",
month="Feb",
day="25",
volume="8",
number="1",
pages="e36884",
keywords="outpatient",
keywords="digital dermatology",
keywords="consultation survey",
abstract="Background: In Scotland, dermatology outpatient services deliver over 300,000 appointments each year. With a significant growth in both new and return attendances, there is an increasing drive for innovative transformation. In response to this challenge, a Digital Dermatology Asynchronous (DDA) consultation platform was co-developed with two National Health Service Dermatology teams. Roll-out of the platform was accelerated during Scotland's initial COVID-19 lockdown and its wider scope was prospectively evaluated. Objective: The aims of the platform were to (1) improve the patient experience by reducing the need to attend hospital for consultations; (2) modernize delivery of outpatient care, providing clinicians with a store-and-forward form of telemedicine; (3) use an integrated digital platform---linked with booking systems and the electronic patient records---to increase efficiency and capacity, thereby creating a more sustainable outpatient service; and (4) create a positive environmental impact by reducing travel and hence the carbon footprint. Methods: During an 11-week ``lockdown'' period from late March 2020, a total of 405 consultations were prospectively audited. Clinicians were asked to complete data collection proformas for each consultation detailing patient demographics, quality of images, diagnosis, and outcomes. The time taken to complete each virtual consultation was recorded for 312 consultations. Feedback surveys were completed by patients and clinicians via email. Results: Of the 405 consultations, 297 new and 108 returning patient consultations were assessed, with 80\% of submitted images being of satisfactory quality. In total, 292 consultations involved the assessment of lesions, with most referred as suspected cancers. Patients of all ages participated, with 31\% of them being aged over 60 years and the parents of 12 children. The consultations were, on average, 3 minutes shorter than equivalent face-to-face (F2F) interactions, and a total of 5758 km of patient travel was avoided. Outcomes included virtual review (16\%), F2F review (47\%), direct to surgery (11\%), discharge (22\%), and other treatment or investigation (4\%). The majority of those needing F2F review were scheduled for routine follow-up. Patient satisfaction was high, with 82\% of respondents reporting ease of use. Conclusions: The COVID-19 pandemic has resulted in a paradigm shift in the way we deliver outpatient care. DDA consultations are now operational in 4 health boards and have been successfully included in the choice of consultation type available for patients, helping to augment service capacity during pandemic recovery. The platform is the first of its kind in Scotland, to be integrated with the hospital booking system and electronic patient record and offering a valuable alternative to F2F, telephone, and video consultations. Conflicts of Interest: None declared. ",
doi="10.2196/36884",
url="https://www.iproc.org/2022/1/e36884"
}


@Article{info:doi/10.2196/36887,
author="Greis, Christian
and Otten, Marina
and Reinders, Patrick",
title="Teledermatology in German-Speaking Countries: Patients' and Physicians' Perspectives",
journal="iproc",
year="2022",
month="Feb",
day="24",
volume="8",
number="1",
pages="e36887",
keywords="teledermatology",
keywords="telemedicine",
keywords="COVID-19",
keywords="pandemic",
keywords="store-and-forward",
keywords="synchronous",
keywords="asynchronous",
abstract="Background: With increasing digitalization and the current pandemic, teledermatology has gained importance in German-speaking countries in recent years. The regulation on remote consultation methods was recently relaxed, allowing for a more widespread introduction of teledermatological health care services. Objective: The aim of this work is to evaluate a store-and-forward (SAF) teledermatology application from the patients' and physicians' perspectives. Methods: We carried out a noncontrolled user survey of the web-based platform derma2go in the course of the remote consultation by German dermatologists. Through the platform, patients with dermatological requests could obtain expert advice within a few hours after entering their medical history and uploading photographs of their skin lesions. Results: A total of 1476 (t1) and 361 (t2) patients and 2207 dermatologist ratings were included within the evaluation. A large proportion of participants were satisfied with the application (t1=83.9\%; t2=81.2\%). Most participants also rated the usability as high (t1=83.0\% satisfied) and were satisfied with the response time of the dermatologists (t1=92.0\% satisfied). In addition, a large majority agreed with the statement that they trusted the web-based application (t1=90.5\%). At t2, 20.0\% of those who participated stated that their skin problem had healed; for 49.8\% of participants, it had already improved; for 22.0\% of participants, it was unchanged; and for 3.5\% of participants, skin problems had worsened. For 64.0\% of users, the request was completely resolved, and for 24.2\% or users, it was partly resolved as result of the consultation. For 79.7\% of users, no additional information was needed by the participating dermatologists. From the practitioners' perspective, 71.2\% of all requests were completely resolved and 24.7\% were partly resolved. Conclusions: Our evaluation has shown that SAF applications, exemplified by derma2go, are likely to improve access to dermatological care, with a high patient satisfaction and a high rate of resolved requests, from the patients' and physicians' perspectives. In the future, teledermatological SAF applications can represent a supplement to the existing routine care in dermatology. The indications, patient groups, and use cases, for which the application is particularly suitable, will be determined in further studies. ",
doi="10.2196/36887",
url="https://www.iproc.org/2022/1/e36887"
}


@Article{info:doi/10.2196/36890,
author="Caffery, J. Liam",
title="The Role of Standards in Accelerating the Uptake of Artificial Intelligence in Dermatology",
journal="iproc",
year="2022",
month="Feb",
day="23",
volume="8",
number="1",
pages="e36890",
keywords="dermatology",
keywords="artificial intelligence",
keywords="DICOM",
keywords="standards",
keywords="imaging",
abstract="Background: The use of artificial intelligence (AI) for dermatology is showing great promise in research contexts. However, the clinical use of AI in dermatology is still limited. The uptake of medical imaging standards for dermatology imaging is also limited. Standards adoption is more widespread in other imaging specialties (eg, radiology) as is the clinical use of AI. Digital Image Communication in Medicine (DICOM) is the standard for medical imaging. DICOM standardizes image formats and associated metadata. Further, DICOM facilitates interoperability between actors in the digital health ecosystem. Objective: This study aimed to identify how medical imaging standards, in particular DICOM, can support the clinical use of AI. Methods: Design Science Research Methodology was used to determine the role of DICOM in AI-based medical imaging workflows. Scenarios were identified and synthesized using expert consensus. Results: The key benefits of using DICOM to improve the clinical use of AI were the potential to encode artefacts derived from the AI process as DICOM objects and store them alongside the original images. Such objects include downsized or down-sampled images, segmentation objects, or visual explainability maps (eg, class activation maps). DICOM can facilitate interoperability between actors in the medical imaging workflow pipeline and permits the inclusion of AI evidence creators in this pipeline. Owing to standardized image formats and metadata, DICOM can be beneficial for the curation of multi-institutional data sets. The key challenge of using DICOM is limited uptake in some specialties including dermatology. Conclusions: DICOM offers potential to accelerate the clinical adoption of AI in dermatology by addressing several technological issues. More widespread uptake of DICOM in dermatology imaging is required to achieve this potential. Conflicts of Interest: None declared. ",
doi="10.2196/36890",
url="https://www.iproc.org/2022/1/e36890"
}


@Article{info:doi/10.2196/36900,
author="Tan, Sern-Ting Eugene",
title="Teledermatology: Experience in Singapore",
journal="iproc",
year="2022",
month="Feb",
day="21",
volume="8",
number="1",
pages="e36900",
keywords="teledermatology",
keywords="teleconsultation",
keywords="synchronous",
keywords="asynchronous",
keywords="store-and-forward",
keywords="hybrid",
keywords="Singapore",
abstract="Background: The COVID-19 pandemic has accelerated the development and widespread adoption of teledermatology both locally and globally. As dermatology is predominantly a visual specialty, teledermatology is particularly useful for patient care and collaboration between health care professionals. Objective: To share lessons learned from the local experience with teledermatology in Singapore. Methods: The main models of teledermatology are asynchronous (store-and-forward), synchronous (real-time communication), and hybrid teledermatology (mixed combination of both asynchronous and synchronous elements). Results: During the pandemic, teledermatology has enabled suitable patients to have continued access to clinical care in the comfort of their home, while at the same time supporting safe distancing measures to mitigate exposure to and spread of SARS-CoV-2. At the National Skin Centre in Singapore, asynchronous store-and-forward teledermatology is used for telecollaboration with doctors and nurses from external health care institutions, nursing homes, and primary care clinics. A hybrid model comprising synchronous phone or video teleconsultation with the patient, together with review of recent clinical photographs submitted by the patient, is used for the remote care of selected patients with mild and/or stable dermatological conditions. There is a high diagnostic concordance of 87\% between teleconsultation and in-person consultation. As not all patients are suitable for teleconsultation, preteleconsultation triage is helpful. Conclusions: Moving forward, even as we approach a new postpandemic era, teledermatology will continue to evolve and become an integral pillar of the health care landscape. Conflicts of Interest: None declared. ",
doi="10.2196/36900",
url="https://www.iproc.org/2022/1/e36900"
}


@Article{info:doi/10.2196/36904,
author="Thomas, Jayarkar",
title="The Practice of Teledermatology Before, During, and After the COVID-19 Pandemic",
journal="iproc",
year="2022",
month="Feb",
day="18",
volume="8",
number="1",
pages="e36904",
keywords="online consultations",
keywords="telemedicine",
keywords="teledermatology",
keywords="COVID-19",
abstract="Background: Since the beginning of the COVID-19 pandemic, the use of telemedicine has quickly expanded in many countries as clinical frameworks have been forced to shift to virtual platforms to guarantee the safety of patients and staff. Teledermatology, specifically, is well-suited for telemedicine, with evidence supporting its viability, even-handed quality and precision, and cost adequacy in comparison to in-person visits. Teledermatology holds extraordinary potential for expanding access to patients and guaranteeing coherence of care, especially for those from rural and underserved regions. Objective: The aim of this research is to study the practice of teledermatology before, during, and after the COVID-19 pandemic. Methods: A literature search using the following keywords was done: online consultations, teledermatology, post-covid. Reports from integrated health care companies (eg, Practo) were also considered. Results: According to the reports, Indians consulted physicians 10 times more during the second wave (April to May 2021) of the pandemic than in pre--COVID-19 times (January to February 2020). India experienced a record 30-fold spike in web-based physician consultations for COVID-19--related symptoms during this time, as compared to a 6-fold increase during the previous peak. More than 50\% of all web-based consultations were for pulmonologists and general physicians for queries related to COVID-19 and the seasonal flu. Other key specialties that were consulted during this period included gynecology (10\%), dermatology (8\%), and pediatrics (5\%). The demand for general physicians and pulmonologists was at an all-time high, according to the data. Cutaneous manifestations were varied, and included urticaria, varicella-like vesicles, transient livedoid eruptions, livedoid vasculopathy, purpuric eruptions, lichenoid photodermatitis, erythroderma, photocontact dermatitis, and generalized pustular figurate erythema. Conclusions: Continued advocacy efforts and future studies highlighting teledermatology's impact, particularly on minorities, underserved patient populations, and in resource-poor settings, are critical for long-term legislative changes to occur and to provide coverage to our most vulnerable patients. This presentation underscores the state of teledermatology prior to the pandemic, the legal statutory changes that permitted teledermatology to rapidly expand during the pandemic, and the significance of continued work after the pandemic. In short, the interruption of everyday life worldwide caused by SARS-CoV-2 has demonstrated that our method of practicing medicine needs reexamining. Conflicts of Interest: None declared. ",
doi="10.2196/36904",
url="https://www.iproc.org/2022/1/e36904"
}


@Article{info:doi/10.2196/36891,
author="Malvehy, Josep",
title="Risks and Benefits of Artificial Intelligence in Teledermatology",
journal="iproc",
year="2022",
month="Feb",
day="18",
volume="8",
number="1",
pages="e36891",
keywords="artificial intelligence",
keywords="deep learning",
keywords="convolutional neural networks",
keywords="teledermatology",
keywords="risks",
keywords="benefits",
abstract="Background: Recently, deep convolutional neural networks (DCNNs) became of interest as decision support systems for dermoscopic and clinical analysis of skin diseases. Application of artificial intelligence in teledermatology (TD) has been recently reported in several studies as a tool for augmented intelligence. Objective: In this session, a critical discussion of the opportunities, limitations, and risks of AI in TD will be presented with special attention to recent published studies. Methods: We reviewed the literature in PubMed and EMBASE databases in the period of January 2018 to November 2021 with the search terms of dermatology, skin cancer, deep learning, and AI (review of the Regulation of Medical Devices, EU 2017/745). Results: A clear definition of the clinical use of AI in TD has to be considered: primary TD from patients to nurses, primary care physicians or general dermatologists; secondary TD from primary care physicians or nurses to dermatologists; or tertiary TD from dermatologists to hospital dermatologists. In some health models of TD for nurses or primary care physicians, AI assistance can lower the rates of recommending a biopsy or specialist referral, increase self-reported diagnostic confidence, and help to achieve higher diagnostic agreement rates (with dermatologists) in nonreferred cases. The main limitations of the use of AI in TD are the lack of large longitudinal studies, the lack of interpretability of the CNN, biases in the databases and unrepresented dermatological conditions for training, limited representation of different ethnicities, standardization of clinical information and of the images, liability, and privacy issues. How to implement the concept of augmented intelligence in clinical practice with referral TD consultations including structured clinical information and good-quality images will need further research and education among end users. Even if the interface is used for either store-and-forward or live TD, interactive TD is, in principle, straightforward for AI systems, and different TD modalities have particular technological requirements that can reduce their efficacy. Finally, AI systems in TD are under the umbrella of medical device regulatory frames, and specific certification is compulsory. This regulation has the benefit of assuring the quality of the new AI systems and diminishing their risks, but it can simultaneously delay the incorporation of AI tools in clinical practice. Conclusions: AI has the potential to improve the results of the technology in different aspects in multiple modalities of TD. However, the evidence is weak, and several barriers and limitations have to be resolved for their integration in clinical practice. Conflicts of Interest: None declared. ",
doi="10.2196/36891",
url="https://www.iproc.org/2022/1/e36891"
}


@Article{info:doi/10.2196/36888,
author="Schopf, Thomas",
title="eHealth in Norway Before and After the COVID-19 Pandemic",
journal="iproc",
year="2022",
month="Feb",
day="16",
volume="8",
number="1",
pages="e36888",
keywords="COVID-19",
keywords="pandemic",
keywords="teledermatology",
keywords="telemedicine",
keywords="general practitioner",
abstract="Background: Regular teledermatology services were implemented in Norway in the early 1990s. Based on the available technology at the time, live interactive video consultation systems were implemented to facilitate remote consultations between dermatologists and general practitioners. With the introduction of digital cameras some years later, store-and-forward systems were introduced, but the live video systems remained popular. In the 2000s and early 2010s, there were few changes in the volume of Norwegian teledermatology services. During the 2010s, private teledermatology companies emerged, which provided both store-and-forward and live interactive video consultations. While previous services involved specialists and general practitioners, the new services now offered to patients enable them to interact with dermatologists directly. Objective: This lecture aimed to provide a brief overview of the development of telemedicine in Norway before and during the COVID-19 pandemic with special focus on teledermatology. Methods: This lecture provides a brief history of telemedicine in Norway with special attention to the impact of the ongoing COVID-19 pandemic. The content is based on personal experiences and literature references. Results: The COVID-19 pandemic has had a profound impact on all parts of society. In Norway, it has also affected the way telemedicine is practiced. When the number of new infections increased substantially in early 2020, Norway was under lockdown. This had major consequences on the health care system. In response, the Norwegian government and health authorities strongly encouraged the use of telemedicine and implemented measures to support its use. Since then, there has been a large increase in the number of live video consultations both in specialist and community health care. Conclusions: When the necessary technical infrastructure is in place, the remaining barriers to telemedicine use, such as reimbursement and integration of health care systems, can easily be overcome, which would result in high adoption rates of telemedicine. Conflicts of Interest: TS is a partner of the Norwegian teledermatology provider ``Askin.'' ",
doi="10.2196/36888",
url="https://www.iproc.org/2022/1/e36888"
}


@Article{info:doi/10.2196/36893,
author="Gupta, Somesh",
title="Deep Learning Skin Disease Classifiers: Current Status and Future Prospects",
journal="iproc",
year="2022",
month="Feb",
day="15",
volume="8",
number="1",
pages="e36893",
keywords="artificial intelligence",
keywords="deep learning",
keywords="skin disease classifier",
keywords="skin of color",
keywords="mHealth",
keywords="machine learning",
abstract="Background: Most studies on deep learning skin disease classifiers are done with binary classifications (ie, classifying lesions into malignant and benign). However, dermatology practice involves a large number of inflammatory and infective conditions that are not easily diagnosed by nondermatologist physicians. Objective: The aim of this study is to develop a machine learning--based smartphone app for multiclass skin disease classification and evaluate its performance in different levels of dermatology practice. We will also explore similar studies in the literature. Methods: We developed an artificial intelligence--driven smartphone app for 40 common skin diseases and tested it in primary care, tertiary care, and private practice settings. Results: In the clinical study, the overall top-1 accuracy was 75.07\% (95\% CI 73.75\%-76.36\%), top-3 accuracy was 89.62\% (95\% CI 88.67\%-90.52\%), and the mean area under the curve was 0.90 (SD 0.07). Multimedia Appendix 1 shows the top-1 positive predictive values and negative predictive values from a clinical study of 35 diseases using the developed mobile health app on patients. In the literature, there are very few studies on image-based deep learning multiclass classification of common skin diseases and none of them included evaluations in actual clinical settings. Conclusions: An artificial intelligence--driven smartphone app has the potential to improve the diagnosis and management of skin diseases in patients with skin of color. Nondermatologist, primary care physicians are likely to benefit from having access to such an app. Acknowledgments: Nurithm Labs collaborated with the All India Institute Of Medical Science to develop this smartphone app. Other collaborators in this work include Rashi Pangti, Jyoti Mathur, Vikas Chouhan, Sharad Kumar, Lavina Rajput, Sandesh Shah, Atula Gupta, Ambika Dixit, Dhwani Dholakia, Sanjeev Gupta, Savera Gupta, Mariam George, and Vinod Kumar Sharma. Conflicts of Interest: None declared. ",
doi="10.2196/36893",
url="https://www.iproc.org/2022/1/e36893"
}


@Article{info:doi/10.2196/36908,
author="Janda, Monika
and Silva, V. Carina
and Horsham, Caitlin
and Sinclair, Craig
and O'Hara, Montana
and Baade, Peter
and Soyer, Peter H.",
title="Digital Technology in Skin Cancer Prevention and Early Detection",
journal="iproc",
year="2022",
month="Feb",
day="14",
volume="8",
number="1",
pages="e36908",
keywords="skin cancer",
keywords="prevention",
keywords="mHealth",
keywords="text-delivered intervention",
keywords="engagement",
abstract="Background: Mobile teledermatology is increasingly being used in clinical practice and offers the opportunity to counsel the general public about sun protection and skin cancer early detection. Growing evidence suggests that SMS text messaging interventions are an effective way to reach a large number of people and promote sun protection behaviors. Many medical practices already have SMS text message systems in place to communicate with patients, especially for appointment reminders and information. However, could we use these systems for even better outcomes? If so, how? Objective: This presentation will outline the results of the SunText study, a theory-based SMS text messaging intervention designed to evaluate how often and in what way we could communicate with people at risk of skin cancer to have a beneficial effect on sun protection behaviors, sunburn, and participant engagement. Methods: The SunText study was conducted between February-July 2019 in Queensland, Australia. Volunteer participants aged 18 to 40 years were randomized to 4 different intervention schedules using a Latin square design. The schedules included personalized or interactive messages with constant frequency and personalized and interactive messages with either increasing or decreasing frequency. Outcomes measured were reduction in sunburn and engagement with interactive messages, defined as responding to messages by return text. Results: Compared to baseline, the self-reported sun protection habits index was significantly higher in all 4 interventions (P<.01). Overall, sunburn rates decreased from baseline to the end of the intervention (40.3\% to 7.0\%), and remained significantly below baseline levels (23.5\%) at the 6-month follow-up (P<.01). All 4 interventions achieved reductions in sunburn rates (18\%-48\% reduction) during the intervention period. The overall engagement rate with interactive messages was 71\%. The intervention involving interactive messages with constant frequency achieved the highest engagement rate. The intervention with personalized and interactive messages with increasing frequency had the lowest engagement rate. Conclusions: This study adds to the evidence that text messages targeting sun protection are effective in improving sun protection behaviors and reducing sunburn. Results also suggest higher engagement with constant or decreasing message frequency. Although many clinics already use SMS text messaging for scheduling, this presentation may encourage its extended use to raise awareness of sun protection. Interactive messages could also be integrated into sun protection mobile health apps, and provide an opportunity for engaging in health promotion content. Acknowledgments: This study was funded by a research grant from the Harry J Lloyd Charitable Trust. Conflicts of Interest: HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc, MoleMap Australia Pty Ltd, Blaze Bioscience Inc, Revenio Research Oy and a Medical Advisor for First Derm. All other authors declare no conflicts of interest. ",
doi="10.2196/36908",
url="https://www.iproc.org/2022/1/e36908"
}


@Article{info:doi/10.2196/36903,
author="Rabenja, Rapelanoro Fahafahantsoa",
title="PASSION Project: Data Collection in Madagascar and Guinea",
journal="iproc",
year="2022",
month="Feb",
day="11",
volume="8",
number="1",
pages="e36903",
keywords="pediatric dermatology",
keywords="artificial intelligence",
keywords="phototypes",
abstract="Background: Little data on dermatological conditions presenting on African skin are currently available. This is partly due to the lack of dermatologists in African countries, such as Madagascar and Guinea. There are only 13 dermatologists in Madagascar, or one dermatologist for every 2 million inhabitants. By contrast, the prevalence of common dermatosis is constantly increasing, especially among the pediatric population. According to the World Health Organization, 80\% of these skin problems in Africa are grouped into the following 5 pathologies: atopic dermatitis, dermatophytosis, scabies, impetigo, and insect bites. Objective: In the face of this dilemma, artificial intelligence (AI) is a better tool to collect data on a national scale. Madagascar began participating in the PASSION project in June 2020 and Guinea began participating in January 2021. They join other countries, like Switzerland, Australia, China, India, and Tanzania, who are also using AI in dermatology. This study mainly aimed to compare the 5 pathologies according to the different phototypes characterizing these countries and to collect cases on a national scale that will form a national database. The aim of the data collection is to add 1000 cases per year to the database. Methods: To increase the number of cases included in phototypes III to VI, two countries were included. A total of 6 data collection sites were set up in Madagascar and one was set up in Guinea. Patients were recruited during dermatology consultations. All patients presenting the 5 pathologies were included. A total of 3 platforms were used to collect data: my.crf.one, IntelliStream, and Derma2go. Results: A total of 323 cases are currently included in the database for Madagascar, including 76 cases of scabies, 111 cases of atopic dermatitis, 94 cases of dermatophytosis, 35 cases of impetigo and 11 cases of insect bites. The patients' ages ranged from 2 months to 68 years. A male predominance was noted, with a sex ratio of 1.19 (109 males and 91 females). Phototypes ranged from III to VI. For Guinea, 178 total cases included 32 cases of scabies, 26 cases of atopic dermatitis, 92 cases of dermatophytosis, 3 cases of impetigo, and 25 cases of insect bites. Patients' ages ranged between 1 year and 70 years, with a male predominance, a sex ratio of 1.54 (108 males and 70 females), and a predominance of phototype VI. Conclusions: AI is a data collection solution in Africa. However, high bandwidth is needed to employ AI. Conflicts of Interest: None declared. ",
doi="10.2196/36903",
url="https://www.iproc.org/2022/1/e36903"
}


@Article{info:doi/10.2196/36899,
author="Giavina-Bianchi, Mara",
title="Teledermatology in S{\~a}o Paolo, Brazil",
journal="iproc",
year="2022",
month="Feb",
day="10",
volume="8",
number="1",
pages="e36899",
keywords="teledermatology",
keywords="common skin lesions",
keywords="primary care attention",
abstract="Background: There are places in the world where access to dermatologists can be very challenging and general practitioners may not be well trained in the diagnosis and treatment of skin conditions. Store-and-forward teledermatology may improve access to specialty care, provide accurate diagnoses, and reduce time to treatment, resulting in high patient satisfaction. The early detection and timely treatment of severe skin diseases could prevent adverse health outcomes and death. On the other hand, some skin conditions such as mild atopic dermatitis, acne, and fungal infections could be managed within primary care using teledermatology. Objective: We aimed to (1) evaluate the proportion of individuals who could be assessed in primary care using teledermatology and how this affects the waiting time for an in-person dermatologist appointment and (2) assess the most frequent dermatoses according to demographic data and referrals made by the teledermatologist. Methods: A cross-sectional retrospective study, involving 30,976 individuals and 55,624 skin lesions, was conducted from July 2017-July 2018 in the city of S{\~a}o Paulo. We assessed the frequency of diagnoses and referrals to biopsy, in-person dermatologists, or primary care, and compared the waiting time for an in-person dermatologist appointment before and after the teledermatology implementation. Results: We found that 53\% of the patients were managed by the primary care physician, 43\% were referred to in-person dermatologists, and 4\% were referred directly to biopsy, leading to a reduction in waiting time for in-person appointments of 78\% when compared to the previous period (from 6.7 months to 1.5 months). The most frequent diseases were melanocytic nevus, seborrheic keratosis, acne, benign neoplasms, onychomycosis, atopic dermatitis, solar lentigo, melasma, xerosis, and epidermoid cyst, with significant differences according to sex, age, and referrals (Multimedia Appendix 1A,B). Conclusions: The use of teledermatology as a triage tool significantly reduced the waiting time for in-person visits, improving health care access and using public resources wisely. Knowledge of sex, age, diagnoses, and treatment of common skin conditions can enable the creation of public policies for prevention and orientation of the population, as it can be used to train general physicians to address such cases. Conflicts of Interest: None declared. ",
doi="10.2196/36899",
url="https://www.iproc.org/2022/1/e36899"
}


@Article{info:doi/10.2196/36894,
author="Navarrete-Dechent, Cristian",
title="Teledermatology and Artificial Intelligence",
journal="iproc",
year="2022",
month="Feb",
day="9",
volume="8",
number="1",
pages="e36894",
keywords="teledermatology",
keywords="artificial intelligence",
keywords="diagnosis",
keywords="prospective",
keywords="augmented intelligence",
keywords="COVID-19",
abstract="Background: The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective: The aim of this study is to test the performance of and physicians' preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods: We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician's diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results: The balanced (in-distribution) top-1 accuracy of the algorithm (47.6\%) was comparable to the dermatologists (49.7\%) and residents (47.7\%) but superior to the general practitioners (39.7\%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8\% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6\% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions: AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest: None declared. ",
doi="10.2196/36894",
url="https://www.iproc.org/2022/1/e36894"
}


@Article{info:doi/10.2196/36885,
author="Jain, Ayush
and Way, David
and Gupta, Vishakha
and Gao, Yi
and de Oliveira Marinho, Guilherme
and Hartford, Jay
and Sayres, Rory
and Kanada, Kimberly
and Eng, Clara
and Nagpal, Kunal
and DeSalvo, B. Karen
and Corrado, S. Greg
and Peng, Lily
and Webster, R. Dale
and Dunn, Carter R.
and Coz, David
and Huang, J. Susan
and Liu, Yun
and Bui, Peggy
and Liu, Yuan",
title="Race- and Ethnicity-Stratified Analysis of an Artificial Intelligence--Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners",
journal="iproc",
year="2022",
month="Feb",
day="9",
volume="8",
number="1",
pages="e36885",
keywords="deep learning",
keywords="computer-assisted diagnosis",
keywords="dermatology",
keywords="clinical images",
abstract="Background: Many dermatologic cases are first evaluated by primary care physicians or nurse practitioners. Objective: This study aimed to evaluate an artificial intelligence (AI)-based tool that assists with interpreting dermatologic conditions. Methods: We developed an AI-based tool and conducted a randomized multi-reader, multi-case study (20 primary care physicians, 20 nurse practitioners, and 1047 retrospective teledermatology cases) to evaluate its utility. Cases were enriched and comprised 120 skin conditions. Readers were recruited to optimize for geographical diversity; the primary care physicians practiced across 12 states (2-32 years of experience, mean 11.3 years), and the nurse practitioners practiced across 9 states (2-34 years of experience, mean 13.1 years). To avoid memory effects from incomplete washout, each case was read once by each clinician either with or without AI assistance, with the assignment randomized. The primary analyses evaluated the top-1 agreement, defined as the agreement rate of the clinicians' primary diagnosis with the reference diagnoses provided by a panel of dermatologists (per case: 3 dermatologists from a pool of 12, practicing across 8 states, with 5-13 years of experience, mean 7.2 years of experience). We additionally conducted subgroup analyses stratified by cases' self-reported race and ethnicity and measured the performance spread: the maximum performance subtracted by the minimum across subgroups. Results: The AI's standalone top-1 agreement was 63\%, and AI assistance was significantly associated with higher agreement with reference diagnoses. For primary care physicians, the increase in diagnostic agreement was 10\% (P<.001), from 48\% to 58\%; for nurse practitioners, the increase was 12\% (P<.001), from 46\% to 58\%. When stratified by cases' self-reported race or ethnicity, the AI's performance was 59\%-62\% for Asian, Native Hawaiian, Pacific Islander, other, and Hispanic or Latinx individuals and 67\% for both Black or African American and White subgroups. For the clinicians, AI assistance--associated improvements across subgroups were in the range of 8\%-12\% for primary care physicians and 8\%-15\% for nurse practitioners. The performance spread across subgroups was 5.3\% unassisted vs 6.6\% assisted for primary care physicians and 5.2\% unassisted vs 6.0\% assisted for nurse practitioners. In both unassisted and AI-assisted modalities, and for both primary care physicians and nurse practitioners, the subgroup with the highest performance on average was Black or African American individuals, though the differences with other subgroups were small and had overlapping 95\% CIs. Conclusions: AI assistance was associated with significantly improved diagnostic agreement with dermatologists. Across race and ethnicity subgroups, for both primary care physicians and nurse practitioners, the effect of AI assistance remained high at 8\%-15\%, and the performance spread was similar at 5\%-7\%. Acknowledgments: This work was funded by Google LLC. Conflicts of Interest: AJ, DW, VG, YG, GOM, JH, RS, CE, KN, KBD, GSC, LP, DRW, RCD, DC, Yun Liu, PB, and Yuan Liu are/were employees at Google and own Alphabet stocks. ",
doi="10.2196/36885",
url="https://www.iproc.org/2022/1/e36885"
}


@Article{info:doi/10.2196/36895,
author="Polesie, Sam
and Paoli, John",
title="Interobserver and Human--Artificial Intelligence Concordance in Differentiating Between Invasive and In Situ Melanoma",
journal="iproc",
year="2022",
month="Feb",
day="8",
volume="8",
number="1",
pages="e36895",
keywords="artificial intelligence",
keywords="clinical decision-making",
keywords="melanoma",
keywords="neural networks",
keywords="computer",
keywords="supervised machine learning",
abstract="Background: Machine learning algorithms including convolutional neural networks (CNNs) have recently made significant advances in research settings. Even though several algorithms nowadays are targeted directly to the consumer market, their implementation in clinical practice is still pending. Most melanomas are easy to recognize even without the aid of dermoscopy. Nonetheless, it is often more challenging to discriminate between invasive melanoma and melanoma in situ (MIS) in a preoperative setting even with the assistance of dermoscopy. Although several dermoscopic features suggestive of MIS and invasive melanomas have been presented, their usefulness in a larger setting is limited by how well physicians agree on their presence or absence. Objective: The overarching aims of this research project are to identify useful dermoscopic features to help dermatologists predict melanoma thickness and to develop CNNs that can assist dermatologists in the preoperative assessment of melanoma thickness. The ultimate aim is to develop algorithms that can strengthen patient care, improve clinical decision-making, and be used in routine health care. Methods: We have included dermoscopic images as well as clinical close-up images of invasive melanomas and MIS from our department during the time period of January 1, 2016, to December 31, 2020. Using this image material, we have trained, validated, and tested two separated CNNs based on dermoscopic and clinical close-up images. We have also invited dermatologists to review the test sets and, for a subset of the dermoscopic images, asked them to specify the presence of prespecified dermoscopic features. Subsequently, we compared CNN outputs to the combined dermatologists' output for all lesions and assessed the interobserver agreement for several dermoscopic features. Results: The CNN developed using dermoscopic images performed on par with the invited dermatologists whereas the CNN using clinical close-up images was outperformed by the group of dermatologists. Two dermoscopic features (atypical blue-white structures and shiny white lines) both displayed a moderate to substantial interobserver agreement and were both indicative of invasive melanomas >1.0 mm. Conclusions: CNNs used to differentiate between invasive melanomas and MIS might be an example of a clinically relevant machine learning application, but they need further refinement and evaluation in prospective clinical trials. Only a few dermoscopic features are helpful in distinguishing melanoma thickness. Conflicts of Interest: None declared. ",
doi="10.2196/36895",
url="https://www.iproc.org/2022/1/e36895"
}


@Article{info:doi/10.2196/36892,
author="Jha, Krishna
and Jha, Kumar Anil",
title="Virtual Dermatology and the COVID-19 Pandemic in a Resource-Limited Country Such as Nepal",
journal="iproc",
year="2022",
month="Feb",
day="8",
volume="8",
number="1",
pages="e36892",
keywords="virtual dermatology",
keywords="teledermatology",
keywords="COVID-19",
keywords="pandemic",
keywords="resource-poor setting",
keywords="Nepal",
abstract="Background: The COVID-19 pandemic has caused nationwide lockdown, which led to the disruption of health services. Despite being a rising health care modality in Nepal, virtual dermatology services became an effective tool to provide dermatologic care through web-based consultations throughout the country. Therefore, we assessed the implementation of teledermatology services at our center to provide uninterrupted health services across the country during the pandemic. Objective: This study aimed to evaluate the clinicodemographic profile of patients using teledermatology services and patient acceptance of this service. Methods: A retrospective, single-center, observational study was carried out. Clinicodemographic data from the patients using teledermatology services were obtained and analyzed. A set of questionnaires regarding patients' acceptance of teledermatology services were administered to the patients through a survey via telephone calls, and the obtained data were interpreted. Results: A total of 122 teleconsultations were carried out within the country. The mean age of patients was 33.48 (SD 17.89) years. Of these 122 patients, 79 (64.8\%) were from outside and 43 (35.2\%) were from inside the city where the institute is located. The average distance from the institute to the patients' residence was approximately 144.84 (SD 157.20) km, and the mean travel time was approximately 385.31 (SD 889.52) minutes. In total, 89 patients could be contacted, of whom 81 (91\%) found the service easy to use, 75 (84.3\%) were able to express their problems in a manner similar to that during direct visits, 49 (55.05 \%) thought that the teleconsultation was the same as an in-person visit, 80 (89.9\%) were satisfied, and 85 (95.5\%) agreed to use teledermatology services in the future. Superficial fungal infection was the most common diagnosis (24.6 \%). Newly registered patients were more satisfied than follow-up patients (96.36\% vs 79.41\%, respectively; P=.01). Conclusions: This study highlights the importance of virtual dermatology services to deliver dermatologic care during the pandemic in Nepal. In the future, this program has a promising role in providing health care services to meet the medical needs of patients. Conflicts of Interest: None declared. ",
doi="10.2196/36892",
url="https://www.iproc.org/2022/1/e36892"
}


@Article{info:doi/10.2196/35404,
author="Bui, Colin
and Doutre, Marie-Sylvie
and Taieb, Alain
and Beylot-Barry, Marie
and Joseph, Jean-Philippe
and Dorizy-Vuong, Val{\'e}rie",
title="Accuracy of Store-and-Forward Teledermatology for the Diagnosis of Skin Cancer: The Nouvelle-Aquitaine Experience",
journal="iproc",
year="2021",
month="Dec",
day="23",
volume="7",
number="1",
pages="e35404",
keywords="store-and-forward",
keywords="teledermatology",
keywords="telemedicine",
keywords="skin cancer",
abstract="Background: In Nouvelle-Aquitaine (a French region with a population of almost 6 million), the density of dermatologists is less than 3.8/100,000 inhabitants. This lack of dermatological care is delaying diagnosis and management, especially for skin cancer. The SmartDerm Project is a store-and-forward (SAF) teledermatology platform for primary care in Nouvelle-Aquitaine providing dermatological counselling to general practitioners (GPs). Objective: The main objective was to determine the concordance between the diagnosis of skin cancer made by dermatologists and the pathologists' diagnosis. Methods: GPs in 3 pilot departments of Nouvelle-Aquitaine (Lot-Et-Garonne, Deux-S{\`e}vres, Creuse) sent their dermatology requests using their smartphone, via an app called PAACO/Globule; dermatologists at the University Hospital of Bordeaux answered within 48-72 hours. Consecutive cases of skin cancer suspected by the referent dermatologists during the intervention were included, if the result of biopsy interpreted by a certified pathologist was available at the time of the study. Results: Among the 1727 requests, 163 (9\%) concerned a possible diagnosis of skin cancer and were eligible. For 61 cases, the histopathological findings were not available. Eventually, 93 patients with a total of 102 skin lesions were included. Median age was 75 years (range 26-97 years), with 53\% women. The skin lesions had progressed for 8 months on average (range 0.5-36 months). The median response time was 1 day (range 0-61 days); 65 days (range 1-667 days) elapsed on average between the SAF opinion and the histological sample. Histopathology diagnosed 83 malignant lesions (57 basal cell carcinomas, 69\%; 18 squamous cell carcinomas, 22\%; 6 melanomas, 7\%; 1 cutaneous lymphoma, 1\%; 1 secondary location of a primary cancer, 1\%), 1 precancerous lesion, and 18 benign lesions. The concordance between the opinion of the referent dermatologist and the final pathological finding was 83\% for nonmelanocytic lesions and 67\% for melanocytic lesions. Conclusions: This study showed the reliability of SAF teledermatology in the diagnosis of skin cancer, comparable to literature data in the absence of dermatoscopy. The median delay of about two months between request and histology was an improvement compared to the delay of usual appointments in the intervention area. The lack of data for 61 patients showed that SAF telemedicine requires better coordination and follow-up, especially for the management of skin cancer. With this reservation in mind, teledermatology offers an alternative answer for the triage of patients with skin cancer residing in areas with low medical density. Conflicts of Interest: None declared. ",
doi="10.2196/35404",
url="https://www.iproc.org/2021/1/e35404",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739527"
}


@Article{info:doi/10.2196/35432,
author="Borre, D. Ethan
and Chen, C. Suephy
and Nicholas, W. Matilda
and Cooner, W. Edward
and Phinney, Donna
and Morrison, Amanda
and Combs, Natalie
and Kheterpal, Meenal",
title="Early Implementation and Evaluation of a Teledermatology Virtual Clinic Within an Academic Medical Center",
journal="iproc",
year="2021",
month="Dec",
day="20",
volume="7",
number="1",
pages="e35432",
keywords="teledermatology",
keywords="implementation science",
abstract="Background: Teledermatology can increase patient access; however, its optimal implementation remains unknown. Objective: This study aimed to describe and evaluate the implementation of a pilot virtual clinic teledermatology service at Duke University. Methods: Leaders at Duke Dermatology and Duke Primary Care identified a teledermatology virtual clinic to meet patients' access needs. Implementation was planned over the exploration, preparation, implementation, and sustainment phases. We evaluated the implementation success of teledermatology using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework and prioritized outcome collection through a stakeholder survey. We used the electronic health record and patient surveys to capture implementation outcomes. Results: Our process consisted of primary care providers (PCPs) who sent clinical and dermatoscopic images of patient lesions or rashes via e-communication to a teledermatology virtual clinic, with a subsequent virtual clinic scheduling of a video visit with the virtual clinic providers (residents or advanced practice providers, supervised by Duke Dermatology attending physicians) within 2-5 days. The teledermatology team reviews the patient images on the day of the video visit and gives their diagnosis and management plan with either no follow-up, teledermatology nurse follow-up, or in-person follow-up evaluation. Implementation at 4 pilot clinics, involving 19 referring PCPs and 5 attending dermatologists, began on September 9, 2021. As of October 31, 2021, a total of 68 e-communications were placed (50 lesions and 18 rashes) and 64 virtual clinic video visits were completed. There were 3 patient refusals and 1 conversion to a telephonic visit. Participating primary care clinics differed in the number of patients referred with completed visits (range 2-32) and the percentage of providers using e-communications (range 13\%-53\%). Patients were seen soon after e-communication placement; compared to in-person wait times of >3 months, the teledermatology virtual clinic video visits occurred on average 2.75 days after e-communication. In total, 20\% of virtual clinic video visits were seen as in-person visit follow-up, which suggests that the majority of patients were deemed treatable at the virtual clinic. All patients who returned the patient survey (N=10, 100\%) agreed that their clinical goals were met during the virtual clinic video visits. Conclusions: Our virtual clinic model for teledermatology implementation resulted in timely access for patients, while minimizing loss to follow-up, and has promising patient satisfaction outcomes. However, participating primary care clinics differ in their volume of referrals to the virtual clinic. As the teledermatology virtual clinics scale to other clinic sites, a systematic assessment of barriers and facilitators to its implementation may explain these interclinic differences. Acknowledgments: We are grateful to the Private Diagnostic Clinic and Duke Institute for Health Innovation for their support. Conflicts of Interest: None declared. ",
doi="10.2196/35432",
url="https://www.iproc.org/2021/1/e35432",
url="http://www.ncbi.nlm.nih.gov/pubmed/27762281"
}


@Article{info:doi/10.2196/35438,
author="Palaz{\'o}n Cabanes, Carlos Juan
and Juan Carpena, G.
and Berbegal Garc{\'i}a, L.
and Mart{\'i}nez Miravete, T.
and Palaz{\'o}n Cabanes, B.
and Betlloch Mas, I.",
title="A Validated Questionnaire to Evaluate Primary Care Pediatrician Satisfaction With the Use of Teledermatology",
journal="iproc",
year="2021",
month="Dec",
day="17",
volume="7",
number="1",
pages="e35438",
keywords="teledermatology",
keywords="pediatric dermatology",
keywords="satisfaction questionnaire",
abstract="Background: Teledermatology (TD) is a branch of telemedicine focused on the evaluation of cutaneous lesions by dermatologists remotely, in order to avoid unnecessary in-person consults that could be otherwise resolved by this method, and to shorten the time required for prompt evaluation of cutaneous diseases. Objective: This study aimed to create and validate a questionnaire to evaluate satisfaction with the use of TD among primary care pediatricians (PCPs) and to test the questionnaire in our health area before performing an intervention for the optimization of TD. Methods: We first created a questionnaire based on previous publications. Then, an expert consultation was made before drafting the final version of the questionnaire. We tested it twice among pediatricians of different health areas, with a 1-month gap between both evaluations. Internal consistency, reproducibility, and validity of the questionnaire were evaluated. Finally, the validated questionnaire was tested among the PCPs of our health area, to analyze their responses. Results: We registered 38 questionnaire responses. In all, 30 (78.9\%) PCPs actively used TD several times within a month or a year; none of them used TD daily. Technical and health care quality of TD was mostly considered as good or very good. TD was regarded as similar or even better than face-to-face evaluation for most PCPs, whereas 7.9\% (3/38) of PCPs thought TD was worse than conventional consults. Most PCPs considered TD as an effective, self-learning, and trustable tool, and 10.5\% (4/38) of them identified that pictures captured by mobile phones were a barrier for its use, as it affects patient privacy. Technical problems, absence of exclusive devices for image taking, and delayed answers are some other barriers for TD that need to be overcome. Nonetheless, all PCPs were satisfied with TD, and all of them reported they would continue or start to use this tool. Conclusions: TD has demonstrated to be an efficient tool, as it reduces waiting time and costs for dermatology evaluation, and it increases satisfaction among professionals. With our proposed questionnaire, we validated that quality, usability, efficacy, and satisfaction related to TD in our health area had a positive consideration among PCPs in general, but there still are barriers to overcome. Conflict of Interest: None declared. ",
doi="10.2196/35438",
url="https://www.iproc.org/2021/1/e35438",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739494"
}


@Article{info:doi/10.2196/35431,
author="Jeong, Ki Hyeon
and Park, Christine
and Henao, Ricardo
and Kheterpal, Meenal",
title="Privacy Protection With Facial Deidentification Machine Learning Methods: Can Current Methods Be Applied to Dermatology?",
journal="iproc",
year="2021",
month="Dec",
day="17",
volume="7",
number="1",
pages="e35431",
keywords="artificial intelligence",
keywords="privacy",
keywords="facial deidentification",
keywords="machine learning",
abstract="Background: In the era of increasing tools for automatic image analysis in dermatology, new machine learning models require high-quality image data sets. Facial image data are needed for developing models to evaluate attributes such as redness (acne and rosacea models), texture (wrinkles and aging models), pigmentation (melasma, seborrheic keratoses, aging, and postinflammatory hyperpigmentation), and skin lesions. Deidentifying facial images is critical for protecting patient anonymity. Traditionally, journals have required facial feature concealment typically covering the eyes, but these guidelines are largely insufficient to meet ethical and legal guidelines of the Health Insurance Portability and Accountability Act for patient privacy. Currently, facial feature deidentification is a challenging task given lack of expert consensus and lack of testing infrastructure for adequate automatic and manual facial image detection. Objective: This study aimed to review the current literature on automatic facial deidentification algorithms and to assess their utility in dermatology use cases, defined by preservation of skin attributes (redness, texture, pigmentation, and lesions) and data utility. Methods: We conducted a systematic search using a combination of headings and keywords to encompass the concepts of facial deidentification and privacy preservation. The MEDLINE (via PubMed), Embase (via Elsevier), and Web of Science (via Clarivate) databases were queried from inception to May 1, 2021. Studies with the incorrect design and outcomes were excluded during the screening and review process. Results: A total of 18 studies, largely focusing on general adversarial network (GANs), were included in the final review reporting various methodologies of facial deidentification algorithms for still and video images. GAN-based studies were included owing to the algorithm's capacity to generate high-quality, realistic images. Study methods were rated individually for their utility for use cases in dermatology, pertaining to skin color or pigmentation and texture preservation, data utility, and human detection, by 3 human reviewers. We found that most studies notable in the literature address facial feature and expression preservation while sacrificing skin color, texture, pigmentation, which are critical features in dermatology-related data utility. Conclusions: Overall, facial deidentification algorithms have made notable advances such as disentanglement and face swapping techniques, while producing realistic faces for protecting privacy. However, they are sparse and currently not suitable for complete preservation of skin texture, color, and pigmentation quality in facial photographs. Using the current advances in artificial intelligence for facial deidentification summarized herein, a novel approach is needed to ensure greater patient anonymity, while increasing data access for automated image analysis in dermatology. Conflicts of Interest: None declared. ",
doi="10.2196/35431",
url="https://www.iproc.org/2021/1/e35431",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739470"
}


@Article{info:doi/10.2196/35401,
author="Teoh, Chyng Novell Shu
and Oakley, Amanda",
title="A 9-Year Teledermoscopy Service: Retrospective Service Review",
journal="iproc",
year="2021",
month="Dec",
day="17",
volume="7",
number="1",
pages="e35401",
keywords="dermatology",
keywords="dermoscopy",
keywords="telemedicine",
keywords="skin neoplasm",
keywords="melanoma",
abstract="Background: A teledermoscopy service was established in January 2010, where patients attended nurse-led clinics for imaging of lesions of concern and remote diagnosis by a dermatologist. Objective: The study aimed to review the number of visits, patient characteristics, the efficiency of the service, and the diagnoses made. Methods: We evaluated the waiting time and diagnosis of skin lesions for all patient visits from January 1, 2010, to May 31, 2019. The relationships between patient characteristics and the diagnosis of melanoma were specifically analyzed. Results: The teledermoscopy clinic was attended by 6479 patients for 11,005 skin lesions on 8805 occasions. Statistically significant risk factors for the diagnosis of melanoma/melanoma in situ were male sex, European ethnicity, and Fitzpatrick skin type 2. Attendance was maximal during 2015 and 2016. The seasonal variation in visits 2011-2018 revealed a consistent peak at the end of summer and a dip at the end of winter. In the year 2010, 306 patients attended; 76\% (233/306) of these were discharged to primary care and 24\% (73/306) were referred to hospital for specialist assessment. For patients diagnosed by the dermatologist with suspected melanoma from January 1, 2010, to May 31, 2019, the median waiting time for an imaging appointment was 44.5 days (average 57.9 days, range 8-218 days). The most common lesions diagnosed were benign naevus (2933/11,005, 27\%), benign keratosis (2576/11,005, 23\%), and keratinocytic cancer (1707/11,005, 15\%); melanoma was suspected in 5\% (507/11,005) of referred lesions (Multimedia Appendix 1). The positive predictive value of melanoma/melanoma in situ was 61.1\% (320 true positives and 203 false positives). The number needed to treat (ie, the ratio of the total number of excisions to the number with a histological diagnosis of melanoma/melanoma in situ) was 2.02. Conclusions: Diagnoses were comparable to the experience of other teledermoscopy services. Teledermoscopy using a nurse-led imaging clinic can provide efficient and convenient access to dermatology by streamlining referrals to secondary care and prioritizing patients with skin cancer for treatment. Conflicts of Interest: None declared. ",
doi="10.2196/35401",
url="https://www.iproc.org/2021/1/e35401",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739468"
}


@Article{info:doi/10.2196/35389,
author="Chakiri, Radia
and Lahlou, Laila",
title="Attitudes Toward Artificial Intelligence Among Dermatologists in Morocco: A National Survey",
journal="iproc",
year="2021",
month="Dec",
day="17",
volume="7",
number="1",
pages="e35389",
keywords="artificial intelligence",
keywords="skin",
keywords="dermatology",
keywords="dermatologist",
keywords="Morocco",
abstract="Background: Artificial intelligence (AI) is a hot topic, and the use of AI in our day-to-day lives has increased exponentially. AI is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding that of dermatologists in the diagnosis of skin lesions from clinical and dermoscopic images. However, little is known about the attitudes of dermatologists in Morocco toward AI. Objective: The purpose of this cross-sectional study was to evaluate the attitudes of dermatologists in Morocco toward AI. Methods: An online survey was distributed through Google Forms (Google LLC) to dermatologists in Morocco and was open from January to June 2021. Statistical analysis of the data collected was performed using Jamovi software. Any association for which the P value was <.05 was considered statistically significant. Results: In total, 183 surveys were completed and analyzed. Overall, 79.8\% of respondents were female, and the median age was 35 years (IQR 25-74 years). A total of 30.6\% stated that they were not aware of AI, and 34.4\% had a basic knowledge of AI technologies. Only 7.7\% of the respondents strongly agreed that the human dermatologist will be replaced by AI in the foreseeable future. Of the entire group, 61.8\% agreed or strongly agreed that AI will improve dermatology, and 70\% thought that AI should be part of medical training. In addition, only 32.2\% reported having read publications about AI. Female dermatologists showed more fear pertaining to the use of AI within dermatology (P=.01); this group also suggested that AI has a very strong potential in the detection of skin diseases using dermoscopic images (P=.03). Conclusions: Our results demonstrate an overall optimistic attitude toward AI among dermatologists in Morocco. The majority of respondents believed that it will improve diagnostic capabilities. Conflict of Interest: None declared. ",
doi="10.2196/35389",
url="https://www.iproc.org/2021/1/e35389",
url="http://www.ncbi.nlm.nih.gov/pubmed/27734952"
}


@Article{info:doi/10.2196/35439,
author="Patel, D. Akash
and Rundle, W. Chandler
and Kheterpal, Meenal",
title="Trends in Teledermatology Utilization in the United States",
journal="iproc",
year="2021",
month="Dec",
day="16",
volume="7",
number="1",
pages="e35439",
keywords="teledermatology",
keywords="telehealth",
keywords="DataDerm",
keywords="COVID-19",
abstract="Background: Teledermatology is an effective health care delivery model that has seen tremendous growth over the last decade. This growth can be attributed to a variety of factors, including but not limited to an increased access to dermatologic care for those with socioeconomic or geographic barriers, a reduction in health care costs for both the patient and the physician, and the delivery of high-quality dermatologic care. However, the associated barriers include practice reimbursements, interstate licensing, and liability. Despite these apparent barriers, the emergence of COVID-19 afforded teledermatology a surge of demand and loosened regulations, allowing dermatologists to see higher volumes of teledermatology patients. In this paper, we analyzed the American Academy of Dermatology's DataDerm registry teledermatology utilization and patient demographic trends throughout the COVID-19 pandemic. Objective: The aim of this paper was to characterize national-level teledermatology demographic data in the setting of the COVID-19 pandemic. Methods: National-level data were curated for all practices enrolled in the American Academy of Dermatology's DataDerm registry from April 1, 2020, through June 30, 2021. Encounter utilization rates were collected for visit type (ie, teledermatology versus in person), sex, race, age, insurance provider, and location (ie, in state versus out of state). The aggregate total data, as opposed to individual encounter data, were collected. Results: The proportion of women who utilized services via teledermatology (65,023/98,642, 65.9\%) was greater than that of those who utilized in-person services (29,40,122/50,48,450, 58.2\%). Non-White patients made up a higher percentage of teledermatology utilizers (8920/62,324, 15\%) when compared with in-person utilizers (3,94,580/35,08,150, 11.7\%). Younger patients (aged <40) contributed more to teledermatology service utilization (62,695/75,319, 83.2\%) when compared with in-person services (13,29,218/33,01,175, 40.3\%). Medicare was a larger payor contributor for in-person services (8232/1,53,279, 25.2\%) than for teledermatology services (10,89,777/43,30,882, 5.4\%). Utilization by out-of-state patients was proportionally higher for teledermatology services (19,422/1,33,416, 14.6\%) compared with in-person services (5,80,358/1,38,31,400, 4.2\%). Conclusions: Teledermatology services may reach and benefit certain populations (female, younger patients, those with non-White racial backgrounds, and out-of-state patients) more so than others. These baseline demographics may also serve to highlight populations for potential future teledermatology outreach efforts. Conflict of Interest: None declared. ",
doi="10.2196/35439",
url="https://www.iproc.org/2021/1/e35439"
}


@Article{info:doi/10.2196/35440,
author="Tepedino, Kelly
and Thames, Todd",
title="The Clinical Utility of a Handheld Elastic Scattering Spectroscopy Tool and Machine Learning in the Diagnosis and Referral Management of Skin Cancer by Primary Care Physicians",
journal="iproc",
year="2021",
month="Dec",
day="13",
volume="7",
number="1",
pages="e35440",
keywords="artificial intelligence",
keywords="melanoma detection",
keywords="skin cancer",
keywords="spectroscopy",
abstract="Background: Elastic scattering spectroscopy (ESS) is a noninvasive optical biopsy technique that can distinguish between normal and abnormal tissue in vivo. The handheld device measures ESS spectra of skin lesions and classifies lesions with an output of ``Investigate Further'' or ``Monitor.'' The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. The device performance was also evaluated in an associated clinical study. Objective: The aim of this paper was to establish whether the use of a handheld ESS tool can improve the detection of skin malignancies by evaluating clinical performance while emulating a real-world telemedicine clinical care setting. Methods: The associated clinical study examined an independent test set of 332 lesions in a prospective multicenter study that compared algorithm performance to biopsy results for diagnosing malignant lesions. A total of 50 cases were randomly selected from the study data base (25 malignant and 25 benign lesions). Device performance on these lesions had a 96\% sensitivity. High-resolution digital images and the patient's clinical information including prior skin cancer history, risk factors, and physical examination results were available for evaluation. A total of 57 primary care physicians participated in this study in 2 phases, the first phase with their standard-of-care diagnostic and the second phase regarding their evaluation with the device output. The physicians were educated on the ESS device before evaluating the cases in a random order. Case evaluation included the physician reporting their diagnosis, management decision, and confidence level without the device output in the first phase and with the device output in the second phase. The results were evaluated for sensitivity and specificity with confidence intervals. Results: The diagnostic sensitivity of the readers without and with the use of the handheld ESS device increased significantly from 67\% to 88\% (P<.001). There was no significant difference in specificity at 40\% and 53\% (P=.05). The management sensitivity of the readers increased significantly with and without the use of the device, which, respectively, was 94\% (91\%-96\%) and 81\% (77\%-85\%) (P<.001), suggesting that the use of the device may reduce false negatives by 68\%. Specificity was comparable for management decisions (P=.36) at 31\% compared to 36\% without the device. Conclusions: The use of the handheld ESS device significantly improved diagnostic and management sensitivity over standard-of-care, with comparable specificity. While telemedicine has shown promise in many fields, studies have shown that in-person skin evaluation is superior to telemedicine evaluations; however, integration with this type of tool has the potential to improve early detection. ",
doi="10.2196/35440",
url="https://www.iproc.org/2021/1/e35440"
}


@Article{info:doi/10.2196/35441,
author="Benvenuto-Andrade, Cristiane
and Cognetta, A.
and Manolakos, D.",
title="The Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions for Cancer",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35441",
keywords="melanoma sensitivity",
keywords="elastic scattering spectroscopy (ESS) device",
keywords="malignancy detection",
keywords="machine learning",
keywords="skin lesions",
keywords="cancer",
abstract="Background: Elastic scattering spectroscopy (ESS) is an optical biopsy technique that can distinguish between a normal and abnormal tissue in vivo without the need to remove it. The handheld device measures ESS spectra of skin lesions and classifies lesions as either malignant or benign with an output of ``Investigate Further'' or ``Monitor,'' respectively, with positive results accompanied by a spectral score output from 1 to 10, indicating how similar the lesion is to the malignant lesions the device was trained on. The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. Objective: The purpose of this study was to evaluate the safety and effectiveness of the handheld ESS device in detecting the most common types of skin cancer. Methods: A prospective, single-arm, investigator-blinded, multicenter study conducted at 4 investigational sites in the United States was performed. Patients who presented with skin lesions suggestive of melanoma, basal cell carcinoma, squamous cell carcinoma, and other highly atypical lesions were evaluated with the handheld ESS device. A validation performance analysis was performed with 553 lesions from 350 subjects with algorithm version 2.0. An independent test set of 281 lesions was selected and used to evaluate device performance in the detection of melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Statistical analyses included overall effectiveness analyses for sensitivity and specificity as well as subgroup analyses for lesion diagnoses. Results: The overall sensitivity of the device was 92.3\% (95\% CI: 87.1 to 95.5\%). The sensitivity for subgroups of lesions was 95\% (95\% CI 75.1\% to 99.9\%) for melanomas, 94.4\% (95\% CI 86.3\% to 98.4\%) for BCCs, and 92.5\% (95\% CI 83.4\% to 97.5\%) for SCCs. The overall device specificity was 36.6\% (95\% CI 29.3\% to 44.6\%). There was no statistically significant difference between the dermatologist performance and the ESS device (P=.2520). The specificity of the device was highest for benign melanocytic nevi (62.5\%) and seborrheic keratoses (78.2\%). The overall positive predictive value (PPV) was 59.8\%, and the negative predictive value (NPV) was 81.9\% with the study's malignancy prevalence rate of 51\%. For a prevalence rate of 5\%, the PPV was estimated to be 7.1\%, and the NPV was estimated to be 98.9\%. For a prevalence rate of 7\%, the PPV was estimated to be 9.8\%, and the NPV was estimated to be 98.4\%. For a prevalence rate of 15\%, the PPV was estimated to be 20.3\%, and the NPV was 96.4\%. Conclusions: The handheld ESS device has a high sensitivity for the detection of melanoma, BCC, and SCC. Coupled with clinical exam findings, this device can aid physicians in detecting a variety of skin malignancies. The device output can aid teledermatology evaluations by helping frontline providers determine which lesions to share for teledermatologist evaluation as well as potentially benefitting teledermatologists' virtual evaluation, especially in instances of suboptimal photo quality. Acknowledgments: This study was sponsored by Dermasensor Inc. Conflicts of Interest: None declared. ",
doi="10.2196/35441",
url="https://www.iproc.org/2021/1/e35441",
url="http://www.ncbi.nlm.nih.gov/pubmed/27752094"
}


@Article{info:doi/10.2196/35437,
author="Jalaboi, Raluca
and Orbes Arteaga, Mauricio
and Richter J{\o}rgensen, Dan
and Manole, Ionela
and Bozdog, Ionescu Oana
and Chiriac, Andrei
and Winther, Ole
and Galimzianova, Alfiia",
title="Explainability of Convolutional Neural Networks for Dermatological Diagnosis",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35437",
keywords="dermatology",
keywords="explainability",
keywords="convolutional neural networks",
abstract="Background: Convolutional neural networks (CNNs) are regarded as state-of-the-art artificial intelligence (AI) tools for dermatological diagnosis, and they have been shown to achieve expert-level performance when trained on a representative dataset. CNN explainability is a key factor to adopting such techniques in practice and can be achieved using attention maps of the network. However, evaluation of CNN explainability has been limited to visual assessment and remains qualitative, subjective, and time consuming. Objective: This study aimed to provide a framework for an objective quantitative assessment of the explainability of CNNs for dermatological diagnosis benchmarks. Methods: We sourced 566 images available under the Creative Commons license from two public datasets---DermNet NZ and SD-260, with reference diagnoses of acne, actinic keratosis, psoriasis, seborrheic dermatitis, viral warts, and vitiligo. Eight dermatologists with teledermatology expertise annotated each clinical image with a diagnosis, as well as diagnosis-supporting characteristics and their localization. A total of 16 supporting visual characteristics were selected, including basic terms such as macule, nodule, papule, patch, plaque, pustule, and scale, and additional terms such as closed comedo, cyst, dermatoglyphic disruption, leukotrichia, open comedo, scar, sun damage, telangiectasia, and thrombosed capillary. The resulting dataset consisted of 525 images with three rater annotations for each. Explainability of two fine-tuned CNN models, ResNet-50 and EfficientNet-B4, was analyzed with respect to the reference explanations provided by the dermatologists. Both models were pretrained on the ImageNet natural image recognition dataset and fine-tuned using 3214 images of the six target skin conditions obtained from an internal clinical dataset. CNN explanations were obtained as activation maps of the models through gradient-weighted class-activation maps. We computed the fuzzy sensitivity and specificity of each characteristic attention map with regard to both the fuzzy gold standard characteristic attention fusion masks and the fuzzy union of all characteristics. Results: On average, explainability of EfficientNet-B4 was higher than that of ResNet-50 in terms of sensitivity for 13 of 16 supporting characteristics, with mean values of 0.24 (SD 0.07) and 0.16 (SD 0.05), respectively. However, explainability was lower in terms of specificity, with mean values of 0.82 (SD 0.03) and 0.90 (SD 0.00) for EfficientNet-B4 and ResNet-50, respectively. All measures were within the range of corresponding interrater metrics. Conclusions: We objectively benchmarked the explainability power of dermatological diagnosis models through the use of expert-defined supporting characteristics for diagnosis. Acknowledgments: This work was supported in part by the Danish Innovation Fund under Grant 0153-00154A. Conflict of Interest: None declared. ",
doi="10.2196/35437",
url="https://www.iproc.org/2021/1/e35437",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739472"
}


@Article{info:doi/10.2196/35433,
author="Alarc{\'o}n-Soldevilla, Fernando
and Hern{\'a}ndez-G{\'o}mez, Jos{\'e} Francisco
and Garc{\'i}a-Carmona, Antonio Juan
and Campoy Carre{\~n}o, Celia
and Grimalt, Ramon
and Va{\~n}{\'o}-Galvan, Sergio
and Pardo S{\'a}nchez, Jos{\'e}
and Hern{\'a}ndez G{\'o}mez, Amanda Tamara
and Ruffin Villaoslada, Javier Luis Francisco
and L{\'o}pez Avila, {\'A}ngel
and Allegue Gallego, Javier Fernando
and Arcas-Tunez, Francisco",
title="Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35433",
keywords="artificial intelligence",
keywords="alopecia areata",
keywords="predictor",
keywords="treatment",
abstract="Background: Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7\%-3\% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. Objective: This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. Methods: In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90\% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. Results: In total, 32 of 40 (80\%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81\%) images of those predicted as responders showed a favorable response to the treatment. Conclusions: The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features. Conflicts of Interest: None declared. ",
doi="10.2196/35433",
url="https://www.iproc.org/2021/1/e35433",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739507"
}


@Article{info:doi/10.2196/35429,
author="Bimbi, C{\'e}sar
and Dalla Lana, Flores Daiane
and Brzezinski, Piotr
and Kyriakou, Georgia",
title="Crusted Scabies as a Suitable Disease for Teledermatology: A Study of 2 Cases",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35429",
keywords="crusted scabies",
keywords="teledermatology",
abstract="Background: Teledermatology has been available for several years now, but the COVID-19 pandemic has highlighted its importance, especially in remote communities. Crusted scabies (CS) presents a unique clinical picture that favors telediagnosis. Patients with neurological diseases, as well as homeless, HIV-infected patients and people with impaired immunological function, are at risk. Clusters of CS have been reported in French Guyana, and these were associated with human T-lymphotropic virus infections. CS has also been reported in Aboriginal Australian communities. Objective: Teledermatology is especially useful in cases of CS, as it is a disease that affects areas that are in need of medical services. At the same time, CS presents a unique clinical picture. The objective of this presentation is to fuel the clinical suspicion and detection of patients with this debilitating condition. Methods: Relatives of patient 1 contacted our clinic for teledermatology appointments. General practitioners from health services sent images of the second patient. Results: Case 1 involved an older woman living in a nursing home with Alzheimer disease, which was severe enough to constrain her to bed. We recommended that her relatives (who had sent images) collect skin scrapings in a container. These scrapings were sent to a clinical analysis laboratory where microscopic potassium hydroxide preparation revealed the presence of Sarcoptes mites. Treatment with oral ivermectin and topical permethrin resulted in the complete resolution of the lesions. Case 2 involved a homeless, HIV-positive, 42-year-old male. The images were sent by clinicians from local health services. This patient was also treated with oral ivermectin and permethrin lotion. We recognize that this case would need further diagnostic workup, but it is highly indicative of CS. Conclusions: CS is one of the most suited diseases for the practice of teledermatology for widespread, large, hyperkeratotic fissured plaques covered with abundant, silvery scales for which the expression ``once seen, never forgotten'' is highly applicable. These cases are gratifyingly simple to treat, and patients benefit from rapid clinical improvement. Prompt diagnoses prevent outbreaks of scabies for relatives and medical personnel, since these skin crusts contain large numbers of scabies mites. CS has been increasingly reported but poorly recognized, and it has often been misdiagnosed as psoriasis. Images, such as those shown in this presentation, are unique and are enough to raise strong clinical suspicion. Conflicts of Interest: None declared. ",
doi="10.2196/35429",
url="https://www.iproc.org/2021/1/e35429",
url="http://www.ncbi.nlm.nih.gov/pubmed/27762275"
}


@Article{info:doi/10.2196/35427,
author="Cretu, Stefana
and Salavastru, Maria Carmen",
title="Cultural and Linguistic Adaptation for the Romanian Language Version of the Cardiff Acne Disability Index: A Pilot Study on the Web-Based Experience of Cognitive Debriefing",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35427",
keywords="acne",
keywords="quality of life",
keywords="teledermatology",
keywords="Cardiff Acne Disability Index",
abstract="Background: The Cardiff Acne Disability Index (CADI) is a validated measurement instrument for quality of life evaluation in young patients with acne. The original version was designed in English, and it has been translated to other languages. An adaptation for the Romanian language was lacking. Objective: The main objective of this study was to evaluate the comprehensibility of the Romanian language adaptation of the CADI in a small sample of patients with acne. Methods: Guided by the team at Cardiff University, we conducted the stages of the standardized translation process---forward translation, the reconciliation of translated versions, back translation, and cognitive debriefing. The cognitive debriefing stage involved applying the CADI to a small sample of patients. This was followed by individual interviews in which each question was discussed. Ethical approval was obtained for the cognitive debriefing stage. We administered this measure as a web-based form. The completion times for each individual question and for the entire survey were automatically recorded. The interviews for assessing comprehensibility and suitability for the Romanian language and culture were also held as live, web-based meetings. Results: A total of 7 patients with acne---4 females and 3 males---aged between 19 and 34 years were included. All subjects were native speakers of the Romanian language. They had mild or moderate acne. The mean completion time for the survey was 3.28 minutes. The mean score for the CADI was 5.4286. All participants agreed that the language used in this quality of life measurement instrument was simple, clear, and adequate for their native language. Conclusions: Despite the epidemiologic restrictions against COVID-19, through teledermatology, we achieved cultural adaptation for the CADI in a language that previously lacked a specific tool for assessing quality of life impairment in patients with acne. Conflicts of Interest: CMS receives royalties from Springer Nature, consulting fees from Vichy International, and support for attending meetings from Leo Pharma. SC has no conflicts of interest. ",
doi="10.2196/35427",
url="https://www.iproc.org/2021/1/e35427",
url="http://www.ncbi.nlm.nih.gov/pubmed/27759101"
}


@Article{info:doi/10.2196/35400,
author="Kaur, Jasleen
and Sharma, Priyanka
and Thami, P. G.
and Sethi, Maninder
and Kakar, Shruti",
title="Evaluation of Patient-Initiated Direct Care Mobile Phone--Based Teledermatology During The COVID-19 Pandemic",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35400",
keywords="direct care teledermatology",
keywords="teledermatology",
keywords="hybrid teledermatology",
keywords="patient satisfaction",
keywords="physician confidence",
keywords="COVID-19",
abstract="Background: With advances in telecommunication, especially smartphones, teledermatology services offered by specialists are now being directly requested by the patients themselves. This model is known as patient-initiated, direct care teledermatology. It has been pushed to the forefront due to the COVID-19 pandemic. Objective: The objectives of this study were to determine patients' satisfaction and dermatologists' confidence when a diagnosis was made via direct care mobile phone--based teledermatology. Methods: Patients availing direct care teledermatology services during the COVID-19 pandemic at a tertiary care center were subjected to a questionnaire within 5 days of the teleconsultation to assess patient satisfaction and opinions regarding using this model during and beyond the current COVID-19 pandemic. The dermatologists rated their confidence in making the clinical diagnosis on a scale from 1-10 for every case. Results: Of 437 participants, 419 (95.9\%) were satisfied with this mode of teledermatology. An overwhelming majority (n=428, 97.9\%) felt safe consulting the dermatologist via teleconsultation and not having to visit the hospital during the COVID-19 pandemic. In addition, 269 (61.6\%) patients agreed that they would be happy to use a teledermatology service beyond the COVID-19 pandemic. The dermatologists' confidence score in making an accurate diagnosis ranged from 3 to 10, with a mean of 9.20 (SD 1.12). Conclusions: The high levels of patient satisfaction and dermatologists' confidence scores indicate that direct care mobile phone--based teledermatology may be a useful tool in providing dermatological services in appropriate settings and its use should continue to be explored beyond the COVID-19 pandemic. Conflicts of Interest: None declared. ",
doi="10.2196/35400",
url="https://www.iproc.org/2021/1/e35400",
url="http://www.ncbi.nlm.nih.gov/pubmed/27759104"
}


@Article{info:doi/10.2196/35395,
author="Thompson, Harmony
and Oakley, Amanda
and Jameson, B. Michael
and Bowling, Adrian",
title="Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35395",
keywords="artificial Intelligence",
keywords="triage",
keywords="dermatology",
keywords="skin neoplasms",
keywords="skin diseases",
keywords="sensitivity and specificity",
keywords="delivery of health care",
keywords="primary health care",
keywords="primary prevention",
abstract="Background: Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. Objective: We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). Methods: This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists' diagnoses by consensus (the ``gold standard'') with AI and histology where applicable. Results: Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26\% (95\% CI 93.13\%-99.25\%) and the specificity was 97.92\% (95\% CI 92.68\%-99.75\%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100\% (95\% CI 95.85\%-100\%) and the specificity was 72.22\% (95\% CI 54.81\%-85.80\%). Conclusions: The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care. Acknowledgments: MoleMap NZ, who developed the AI algorithm, provided some funding for this study. HT's salary was partially sponsored by MoleMap NZ, who developed the AI algorithm. AB is a shareholder and consultant to Molemap Ltd provider of the AI algorithm. Conflicts of Interest: None declared. ",
doi="10.2196/35395",
url="https://www.iproc.org/2021/1/e35395",
url="http://www.ncbi.nlm.nih.gov/pubmed/27734949"
}


@Article{info:doi/10.2196/35393,
author="Jones, Leah
and Oakley, Amanda",
title="Are We Missing Something? The Skin Lesions Not Seen in Teledermatology",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35393",
keywords="skin diseases",
keywords="skin neoplasms",
keywords="dermatology",
keywords="telemedicine",
keywords="teledermatology",
abstract="Background: The suspected skin cancer electronic referral pathway was introduced in 2017. It requires general practitioners to add regional, close-up, and dermoscopic images to a lesion-specific referral template for a teledermatologist to review and advise on management. The virtual lesion clinic is a nurse-led clinic used since 2010 to obtain high-quality images for teledermoscopy assessment. A limitation of both services is the absence of a full-body examination. Objective: This study aims to evaluate the number of skin cancers missed during teledermatology assessment. Methods: This is a retrospective review of skin lesion referrals to dermatology. Suspected skin cancer referrals made in the latter half of 2020 were compared with referrals to the virtual lesion clinic during a similar time period in 2016. Results: The study included 481 patients with 548 lesions in the 2020 suspected skin cancer cohort that were matched for age, sex, and ethnicity to 400 patients with 682 lesions in the 2016 virtual lesion clinic cohort. A total of 41 patients underwent subsequent specialist review in the suspected skin cancer cohort compared to 91 in the virtual lesion clinic cohort. A total of 20\% of the suspected skin cancer cohort and 24\% of the virtual lesion clinic cohort were found to have at least one additional lesion of concern. The majority of these were keratinocytic skin cancers; there were 2 and 0 additional melanomas or melanoma-in-situ, respectively. The virtual lesion clinic nurses identified additional lesions for imaging in 78 of 400 (20\%) patients assessed in the virtual lesion clinic. The teledermatologist determined (author AO) that 73\% of these additional lesions were malignant. Of the 548 lesions, 10 (2\%) in the suspected skin cancer group were rereferred, none of which had a change in diagnosis. Out of 682 lesions, 16 (2\%) in the virtual lesion clinic cohort were rereferred, 6 (1\%) of which had a change in diagnosis. Conclusions: Patients diagnosed with skin cancer often have multiple lesions of concern. Single-lesion teledermoscopy diagnoses have high concordance with in-person evaluation and histology; however, we have shown that in-person examination may reveal other suspicious lesions. The importance of a full-body skin examination should be emphasized to the referrer. Acknowledgments: The Waikato Medical Research Foundation provided financial support for the study. Conflicts of Interest: None declared. ",
doi="10.2196/35393",
url="https://www.iproc.org/2021/1/e35393",
url="http://www.ncbi.nlm.nih.gov/pubmed/27739491"
}


@Article{info:doi/10.2196/35391,
author="Oloruntoba, Ibukun
and Nguyen, D. Toan
and Ge, Zongyuan
and Vestergaard, Tine
and Mar, Victoria",
title="Assessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35391",
keywords="teledermatology",
keywords="CNN",
keywords="artificial intelligence",
keywords="skin cancer",
keywords="Denmark",
keywords="Australia",
keywords="New Zealand",
keywords="image standardization",
keywords="generalizability",
keywords="classification",
abstract="Background: Convolutional neural networks (CNNs) are a type of artificial intelligence that show promise as a diagnostic aid for skin cancer. However, the majority are trained using retrospective image data sets of varying quality and image capture standardization. Objective: The aim of our study is to use CNN models with the same architecture, but different training image sets, and test variability in performance when classifying skin cancer images in different populations, acquired with different devices. Additionally, we wanted to assess the performance of the models against Danish teledermatologists when tested on images acquired from Denmark. Methods: Three CNNs with the same architecture were trained. CNN-NS was trained on 25,331 nonstandardized images taken from the International Skin Imaging Collaboration using different image capture devices. CNN-S was trained on 235,268 standardized images, and CNN-S2 was trained on 25,331 standardized images (matched for number and classes of training images to CNN-NS). Both standardized data sets (CNN-S and CNN-S2) were provided by Molemap using the same image capture device. A total of 495 Danish patients with 569 images of skin lesions predominantly involving Fitzpatrick skin types II and III were used to test the performance of the models. Four teledermatologists independently diagnosed and assessed the images taken of the lesions. Primary outcome measures were sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUROC). Results: A total of 569 images were taken from 495 patients (n=280, 57\% women, n=215, 43\% men; mean age 55, SD 17 years) for this study. On these images, CNN-S achieved an AUROC of 0.861 (95\% CI 0.830-0.889; P<.001), and CNN-S2 achieved an AUROC of 0.831 (95\% CI 0.798-0.861; P=.009), with both outperforming CNN-NS, which achieved an AUROC of 0.759 (95\% CI 0.722-0.794; P<.001; P=.009). When the CNNs were matched to the mean sensitivity and specificity of the teledermatologists, the model's resultant sensitivities and specificities were surpassed by the teledermatologists. However, when compared to CNN-S, the differences were not statistically significant (P=.10; P=.05). Performance across all CNN models and teledermatologists was influenced by the image quality. Conclusions: CNNs trained on standardized images had improved performance and therefore greater generalizability in skin cancer classification when applied to an unseen data set. This is an important consideration for future algorithm development, regulation, and approval. Further, when tested on these unseen test images, the teledermatologists clinically outperformed all the CNN models; however, the difference was deemed to be statistically insignificant when compared to CNN-S. Conflicts of Interest: VM received speakers fees from Merck, Eli Lily, Novartis and Bristol Myers Squibb. VM is the principal investigator for a clinical trial funded by the Victorian Department of Health and Human Services with 1:1 contribution from MoleMap. ",
doi="10.2196/35391",
url="https://www.iproc.org/2021/1/e35391",
url="http://www.ncbi.nlm.nih.gov/pubmed/27762282"
}


@Article{info:doi/10.2196/35388,
author="Howard, Lucy
and Jagun, O.
and Hong, A.
and Hassan, Z.
and Wong, C.
and Halpern, S.",
title="Inpatient Teledermatology Referrals During the COVID-19 Pandemic in a UK Trust: A Comparative Review and Doctor Survey",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35388",
keywords="teledermatology",
keywords="acute dermatology",
keywords="COVID-19",
keywords="referrals",
abstract="Background: The COVID-19 pandemic has broadened the scope of teledermatology services in the United Kingdom from a primarily outpatient-based triage tool to the management of inpatient referrals. In order to reduce the risk of transmission in hospital, a number of changes were implemented within our department. As part of this, our on-call referrals were transferred to a telemedicine app, which incorporates the secure transfer of user-generated patient images onto a web-based image management system providing remote access for the dermatology team. Objective: This study aimed to compare how the introduction of this referral method impacted the nature and number of referrals received, the efficiency of the on-call service, and user preferences. Methods: A retrospective cohort study was conducted to compare the number of referrals, time taken to review, and referral diagnoses between previous referral methods to the dermatology department (bleep, fax, email) (July and September 2019) and the new teledermatology app (July and September 2020). We also performed a survey of junior doctors, seeking their feedback and preferences pertaining to the new referral system. Results: The number of referrals increased by 80\%, with a 6-fold increase in lesion referrals. There is a possibility that not all referrals from 2019 were accounted for as paper documents are easily lost or discarded, highlighting another advantage of teledermatology in providing a reliable record of referrals. Dermatology referrals may have increased as the telemedicine app is more accessible to staff across sites. The telemedicine app also led to a reduction in time to review by 0.53 days, resulting in a significantly higher number of patients being given dermatology input on the day of the referral (78\% vs 58\%). This will have led to earlier treatment, improved patient outcomes, and shorter inpatient stays, resulting in potential cost reductions for the hospital. The survey of junior doctors showed that 81\% preferred teledermatology to the previous referral methods. Conclusions: The introduction of teledermatology has provided an effective and acceptable method of managing on-call dermatology referrals. Easier access to dermatology advice via teledermatology may result in higher numbers of referrals, which may warrant strict referral criteria to prevent oversubscription of the on-call service. Teledermatology ensures an accurate log of referrals, including the nature of referrals, allowing for better auditing and service improvement. Teledermatology referrals allow for advice to be provided within shorter time frames compared to previous methods. This should improve patient outcomes and reduce hospital admission stays, potentially resulting in cost savings for the hospital. Conflict of Interest: None declared. ",
doi="10.2196/35388",
url="https://www.iproc.org/2021/1/e35388"
}


@Article{info:doi/10.2196/35386,
author="Chan, R. Erika Kim
and D Melendres, Michelle Jacqueline",
title="Healing From a Distance: A Cross-sectional Study on the Diagnostic Reliability of Store-and-Forward Teledermatology",
journal="iproc",
year="2021",
month="Dec",
day="10",
volume="7",
number="1",
pages="e35386",
keywords="teledermatology",
keywords="concordance",
keywords="reliability",
abstract="Background: Telemedicine delivers health care services between two distant locations through the use of information and communication technology. Several medical specializations, such as dermatology, have incorporated telemedicine into their practice. Since dermatologists are trained to diagnose skin, hair, and nail conditions with a clinical eye, teledermatology may be an alternative when a traditional face-to-face clinic visit is not feasible. Objective: The purpose of this study was to evaluate the diagnostic reliability of teledermatology. Methods: A cross-sectional study was conducted among patients from 2 government hospitals. A total of 39 patients were seen in a face-to-face setting and diagnosed by a consultant dermatologist. A written history of their present illness and accompanying photographs were taken and were shown to 3 consultant teledermatologists, who then diagnosed their condition. Two senior dermatology residents then rated the face-to-face and teledermatology diagnoses as either complete agreement, partial agreement, or no agreement. Descriptive statistics was used to summarize the general and clinical characteristics of the participants. The Cohen kappa was used to assess agreement in the evaluations between the teledermatology and face-to-face diagnoses by senior resident raters \#1 and \#2. Results: Over 70\% of the diagnoses were deemed as either partial or in complete agreement with the face-to-face diagnosis for senior resident rater \#1. Similarly, over 80\% of the diagnoses were deemed as either partial or in complete agreement with the face-to-face diagnosis for senior resident rater \#2. The agreement between the ratings of senior residents \#1 and \#2 ranged from fair to substantial. Conclusions: The findings of the study showed that the diagnostic concordance of in-person clinicians and teledermatologists ranges from fair to substantial, with over 70\% of the diagnoses in partial or complete agreement. Although face-to-face consultations remain the gold standard, teledermatology is an important alternative where dermatologic care is not accessible. Conflicts of Interest: None declared. ",
doi="10.2196/35386",
url="https://www.iproc.org/2021/1/e35386"
}