Deep Learning for Diagnosis of Tinea Corporis and Tinea Cruris

Tinea corporis and tinea cruris are frequently misdiagnosed, and studies utilizing deep learning for their diagnosis remain scarce.

This study primarily aimed to develop a deep learning model trained on images from diverse clinical settings to distinguish dermatophytosis from non-dermatophytosis. A secondary objective was to compare its diagnostic performance with that of physicians.

This retrospective diagnostic study analyzed clinical images of dermatophytosis (tinea corporis/cruris) and non-dermatophytosis (eczema, psoriasis, and lichen planus). Dermatophytosis was confirmed by the presence of hyphae, whereas non-dermatophytosis was diagnosed based on clinical and histological findings. The deep learning model utilized a multitask learning approach, integrating classification and segmentation. Adaptive weighting facilitated area-of-interest segmentation, improving attention to relevant features. Model performance was compared with physicians using a 20-image quiz from the test dataset.

A total of 1400 images (600 dermatophytosis, 840 non-dermatophytosis) from 580 Thai patients, predominantly with Fitzpatrick skin types III and IV, were analyzed. In the test dataset, the model achieved an AUC of 0.84 (95% CI: 0.80-0.88), a sensitivity of 0.80 (95% CI: 0.74-0.85), a specificity of 0.71 (95% CI: 0.65-0.76), an accuracy of 0.74 (95% CI: 0.70-0.78), and an F1-score of 0.71 (95% CI: 0.66-0.76). AUC values for deep learning, dermatologists (n = 15), and non-dermatologists (n = 11) on the image quiz were 0.80, 0.78, and 0.75, respectively.

The deep learning model demonstrated effective diagnostic performance for tinea corporis/cruris in Asian skin types, performing comparably to dermatologists as evaluated using a 20-image quiz derived from the test dataset.