Deep learning-based diagnosis models for onychomycosis in dermoscopy

Abstract

Background: Onychomycosis is a common disease. Emerging noninvasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of onychomycosis. However, deep learning application in dermoscopic images has not been reported.

Objectives: To explore the establishment of deep learning-based diagnostic models for onychomycosis in dermoscopy to improve the diagnostic efficiency and accuracy.

Methods: We evaluated the dermoscopic patterns of onychomycosis diagnosed at Sun Yat-sen Memorial Hospital, Guangzhou, China, from May 2019 to February 2021 and included nail psoriasis and traumatic onychodystrophy as control groups. Based on the dermoscopic images and the characteristic dermoscopic patterns of onychomycosis, we gain the faster region-based convolutional neural networks to distinguish between nail disorder and normal nail, onychomycosis and non-mycological nail disorder (nail psoriasis and traumatic onychodystrophy). The diagnostic performance is compared between deep learning-based diagnosis models and dermatologists.

Results: All of 1,155 dermoscopic images were collected, including onychomycosis (603 images), nail psoriasis (221 images), traumatic onychodystrophy (104 images) and normal cases (227 images). Statistical analyses revealed subungual keratosis, distal irregular termination, longitudinal striae, jagged edge, and marble-like turbid area, and cone-shaped keratosis were of high specificity (>82%) for onychomycosis diagnosis. The deep learning-based diagnosis models (ensemble model) showed test accuracy /specificity/ sensitivity /Youden index of (95.7%/98.8%/82.1%/0.809) and (87.5%/93.0%/78.5%/0.715) for nail disorder and onychomycosis. The diagnostic performance for onychomycosis using ensemble model was superior to 54 dermatologists.

Conclusions: Our study demonstrated that onychomycosis had distinctive dermoscopic patterns, compared with nail psoriasis and traumatic onychodystrophy. The deep learning-based diagnosis models showed a diagnostic accuracy of onychomycosis, superior to dermatologists.

Keywords: artificial intelligence; deep learning; dermoscopy; faster R-CNN; nail disorder; onychomycosis.