R Pangti1, J Mathur2, V Chouhan2, S Kumar2, L Rajput1, S Shah1, A Gupta3, A Dixit1, D Dholakia4,5, S Gupta1, S Gupta1, M George7, V K Sharma1, S Gupta1. 1. Department of Dermatology and Venereology, All India Institute of Medical Science, New Delhi, India. 2. Nurithm Labs Private Limited, Noida, India. 3. Skin Aid Clinic, Cross Point Mall, Gurugram, India. 4. Genomics and Molecular Medicine Unit, Academy of Scientific and Innovative Research, New Delhi, India. 5. Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India. 6. Maharishi Markandeshwar Institute of Medical Sciences and Research, Mullana, Ambala, India. 7. Sahrudya Hospital, Alappuzha, India.
Abstract
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.
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.