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Updated: Oct 4, 2025

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

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Deep learning-based diagnosis models for onychomycosis in dermoscopy.

Xianzhong Zhu^1,2, Bowen Zheng¹, Wenying Cai¹

¹Department of Dermatology and Venereology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.

|February 4, 2022

Summary

Deep learning models accurately diagnose onychomycosis using dermoscopic images, outperforming dermatologists. This AI approach enhances diagnostic efficiency and accuracy for fungal nail infections.

Keywords:

artificial intelligence deep learning dermoscopy faster R-CNN nail disorder onychomycosis

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Area of Science:

Dermatology and Artificial Intelligence
Medical Imaging Analysis
Computational Pathology

Background:

Onychomycosis is a prevalent fungal nail infection.
Dermoscopy and deep convolutional neural networks (CNNs) are emerging diagnostic tools.
The application of deep learning in dermoscopic onychomycosis diagnosis remains unexplored.

Purpose of the Study:

To develop deep learning-based diagnostic models for onychomycosis using dermoscopic images.
To enhance the diagnostic efficiency and accuracy of onychomycosis detection.
To compare the performance of AI models against dermatologists.

Main Methods:

Evaluation of 1,155 dermoscopic images from onychomycosis, nail psoriasis, and traumatic onychodystrophy cases.
Utilized faster region-based convolutional neural networks (R-CNNs) to differentiate nail disorders.
Compared diagnostic performance between AI models and 54 dermatologists.

Main Results:

Identified specific dermoscopic patterns (e.g., subungual keratosis, longitudinal striae) highly indicative of onychomycosis.
The ensemble deep learning model achieved high accuracy (95.7%) for nail disorders and (87.5%) for onychomycosis.
The AI model demonstrated superior diagnostic performance for onychomycosis compared to dermatologists.

Conclusions:

Onychomycosis exhibits distinct dermoscopic patterns differentiating it from nail psoriasis and traumatic onychodystrophy.
Deep learning models applied to dermoscopic images offer a highly accurate and efficient method for onychomycosis diagnosis.
AI-powered diagnosis shows potential to surpass dermatologists' accuracy in identifying fungal nail infections.