Accuracy of Deep Neural Network in Triaging Common Skin Diseases of Primary Care Attention

Access to dermatological care can be challenging in certain regions of the world. The triage process is usually conducted by primary care physicians; however, they may not be able to diagnose and assign the correct referral and level of priority for different dermatosis. The present research aimed to test different deep neural networks to obtain the highest level of accuracy for the following: (1) diagnosing groups of dermatoses; (2) correct referrals; and (3) the level of priority given to the referral compared to dermatologists. Using 140,446 images from a teledermatology project, previously labeled with the clinical diagnosis, and their respective referrals, namely biopsy, in-person dermatologist visits or monitoring the case via teledermatology along with the general physician, 27 different scenarios of neural networks were derived, and the algorithm accuracies in classifying different dermatosis, according to the group of the diagnosis they belong to, were calculated. The most accurate algorithm was then tested for accuracy in diagnosis, referral, and level of priority given to 6,945 cases. The GoogLeNet architecture, trained with 24,000 images and 1,000 epochs, using weight random initialization and learning rates of 10(-3) was found to be the most accurate network, showing an accuracy of 89.72% for diagnosis, 96.03% for referrals and 92.54% for priority level in 6,975 image testing. Our study population, however, was confined to individuals with chronic skin conditions and, therefore, it has limited value as a triage tool because it has not been tested for acute conditions. Deep neural networks are accurate in triaging, correct referral and prioritizing common chronic skin diseases related to primary care attention. They can also help health-care systems optimize patients' access to dermatologists.