Background The integration of machine learning algorithms in decision support tools for physicians is gaining popularity. These tools can tackle the disparities in healthcare access as the technology can be implemented on smartphones. We present the first, large‐scale study on patients with skin of colour, in which the feasibility of a novel mobile health application (mHealth app) was investigated in actual clinical workflows. Objective To develop a mHealth app to diagnose 40 common skin diseases and test it in clinical settings. Methods A convolutional neural network‐based algorithm was trained with clinical images of 40 skin diseases. A smartphone app was generated and validated on 5014 patients, attending rural and urban outpatient dermatology departments in India. The results of this mHealth app were compared against the dermatologists’ diagnoses. Results The machine–learning model, in an in silico validation study, demonstrated an overall top‐1 accuracy of 76.93 ± 0.88% and mean area‐under‐curve of 0.95 ± 0.02 on a set of clinical images. In the clinical study, on patients with skin of colour, the app achieved an overall top‐1 accuracy of 75.07% (95% CI = 73.75–76.36), top‐3 accuracy of 89.62% (95% CI = 88.67–90.52) and mean area‐under‐curve of 0.90 ± 0.07. Conclusion This study underscores the utility of artificial intelligence‐driven smartphone applications as a point‐of‐care, clinical decision support tool for dermatological diagnosis for a wide spectrum of skin diseases in patients of the skin of colour.

Reported cutaneous manifestations of SARS‐Cov‐2 infection include maculopapular rash, urticarial rash, varicelliform or vesicular lesions, petechiae/purpura, livedoid/necrotic lesions, chilblains‐like lesions (Covid toes), erythema multiforme‐like lesions, 1 and aphthous ulcers. 2 These cutaneous manifestations have been mostly reported from countries with Caucasian populations with a paucity of data from the skin‐of‐color populations. 3 We conducted a prospective study to report the prevalence and patterns of cutaneous manifestations in COVID‐19 patients from India.

Background Vitiligo manifests as hypo-to de-pigmented macules, which are sometimes associated with leukotrichia. For complete cosmetic improvement, the repigmentation of leukotrichia is an important component. Methods This randomized controlled trial included patients with stable vitiligo with leukotrichia. Two vitiligo patches in each patient were randomized to receive either of the two procedures. The patients were followed up for 9 months posttransplantation. The efficacy of hair follicle cell suspension (HFCS) with epidermal cell suspension (ECS) in repigmentation of leukotrichia and skin in vitiligo was compared. Results A total of 20 patients underwent the procedure, and 19 completed the follow-up. The area of the vitiligo patch and the number of leukotrichia in the patches were comparable between the two groups. There was a significant difference in the mean AE S.D. number of cells transplanted between the two groups (5.06 9 10 5 in HFCS vs. 39.8 9 10 5 in ECS, P < 0.0001). The percentage viability of cells and proportion of melanocytes were comparable between the two groups. A total of 10 patients in HFCS and eight patients in ECS had repigmentation of leukotrichia. The mean AE S.D. percentages of depigmented hair showing repigmentation at nine months were 7.42 AE 11.62% in HFCS and 11.42 AE 17.90% in ECS (P = 0.4195), whereas the mean AE S.D. percentage repigmentation of vitiligo patches was 61.58 AE 42.68% in HFCS and 78.68 AE 30.03% in ECS (P = 0.1618). Conclusions The mean number of cells transplanted in the HFCS group was about eight times less than those in ECS. ECS was better than HFCS in repigmentation of leukotrichia and vitiligo, although the difference was not statistically significant.

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During the COVID-19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID-19-related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID-19-associated skin lesions from clinical images. An ensemble model of three different CNN-based algorithms was trained with clinical images of skin lesions from confirmed COVID-19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID-19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi-class model demonstrated an overall top-1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID-19-rash detection were found to be 84.2 ± 5.1% and 99.5 ± 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID-19-rash were 88.0 ± 5.6%, 99.4 ± 0.2% and 0.97 ± 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 ± 6.25%, 99.77 ± 0.14% and 98.91 ± 0.17%, respectively for COVID-19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID-19-associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning-based study for automated detection of COVID-19 based on skin images and may provide a useful decision support tool for physicians to optimize contact-free COVID-19 triage, differential diagnosis of skin lesions and patient care.