JMIR Publications

ABSTRACT

Background:

Private-part skin diseases (PPSDs) can cause a patient's stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs.

Objective:

To develop and evaluate an AI-aided diagnosis system for the detection and classification of PPSDs.

Methods:

In this study, a two-stage AI-aided diagnosis system was developed to classify PPSDs. A reader study with 13 dermatologists of different experience levels was conducted. Dermatologists were asked to classify the testing cohort under reading room conditions, first without and then with system support. This AI-aided diagnostic study used the data of 635 patients from 2 institutes between July 2019 and April 2022. The data of institute 1 contained 2,701 skin lesion samples of 520 patients, which was used for the training of the multi-task detection network in the first stage. In addition, the data of institute 2 consisted of 115 clinical images and the corresponding medical records, which was used for the test of the whole two-stage AI-aided diagnosis system.

Results:

On the test data of institute 2, the proposed system achieved the average precision, recall, and F1-score of 0.81, 0.86, and 0.83, respectively, better than existing advanced algorithms. For the reader performance test, our system improved the average F1-score of the junior, intermediate, and senior dermatologists by 16%, 7%, and 4%, respectively.

Conclusions:

In this study, we constructed the first skin-lesion-based data set and developed the first AI-aided diagnosis system for PPSDs. This system provides the final diagnosis result by simulating the diagnostic process of dermatologists. Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. Overall, our system can not only help patients achieve self-screening and alleviate their stigma, but also assist dermatologists in diagnosing PPSDs.