In Journal of the European Academy of Dermatology and Venereology : JEADV
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-color, 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 5,014 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 color, 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 color.
Pangti Rashi, Mathur Jyoti, Chouhan Vikas, Kumar Sharad, Rajput Lavina, Shah Sandesh, Gupta Atula, Dixit Ambika, Dholakia Dhwani, Gupta Sanjeev, Gupta Savera, George Mariam, Sharma Vinod Kumar, Gupta Somesh
Artificial Intelligence, Community Dermatology, Machine Learning, Pattern recognition, mHealth