Iproceedings

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).

Teledermatology can increase patient access; however, its optimal implementation remains unknown.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.