Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis

• Published in: PloS one Vol. 15; no. 6; p. e0234334
• Main Authors: Kim, Young Jae, Han, Seung Seog, Yang, Hee Joo, Chang, Sung Eun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.06.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Adult; Algorithms; Antifungal agents; Artificial neural networks; Biology and Life Sciences; Comparative analysis; Comparative studies; Computer aided medical diagnosis; Computer and Information Sciences; Computer Systems; Deep Learning; Dermatologists; Dermatology; Dermoscopy; Diagnosis; Diagnosis, Computer-Assisted; Diagnostic Errors; Diagnostic systems; Female; Foot Dermatoses - diagnosis; Foot Dermatoses - diagnostic imaging; Foot Dermatoses - microbiology; Fungal infections; Ground truth; Hospitals; Humans; Hydroxides; Machine learning; Male; Medical diagnosis; Medical microscopy; Medicine and Health Sciences; Methods; Microscopy; Middle Aged; Mycological Typing Techniques; Neural networks; Neural Networks, Computer; Observational studies; Onychomycosis; Onychomycosis - diagnosis; Onychomycosis - diagnostic imaging; Onychomycosis - microbiology; Photography; Physical Sciences; Potassium; Potassium Compounds; Potassium hydroxide; Potassium hydroxides; Predictive Value of Tests; Prospective Studies; Research and Analysis Methods; Sensitivity; Studies; Toenail; Young Adult; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0234334

Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. However, comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted. This study evaluated the diagnostic abilities of a deep neural network (http://nail.modelderm.com) and dermoscopic examination in patients with onychomycosis. A prospective observational study was performed in patients presenting with dystrophic features in the toenails. Clinical photographs were taken by research assistants, and the ground truth was determined either by direct microscopy using the potassium hydroxide test or by fungal culture. Five board-certified dermatologists determined a diagnosis of onychomycosis using the clinical photographs. The diagnosis was also made using the algorithm and dermoscopic examination. A total of 90 patients (mean age, 55.3; male, 43.3%) assessed between September 2018 and July 2019 were included in the analysis. The detection of onychomycosis using the algorithm (AUC, 0.751; 95% CI, 0.646-0.856) and that by dermoscopy (AUC, 0.755; 95% CI, 0.654-0.855) were seen to be comparable (Delong's test; P = 0.952). The sensitivity and specificity of the algorithm at the operating point were 70.2% and 72.7%, respectively. The sensitivity and specificity of diagnosis by the five dermatologists were 73.0% and 49.7%, respectively. The Youden index of the algorithm (0.429) was also comparable to that of the dermatologists' diagnosis (0.230±0.176; Wilcoxon rank-sum test; P = 0.667). As a standalone method, the algorithm analyzed photographs taken by non-physician and showed comparable accuracy for the diagnosis of onychomycosis to that made by experienced dermatologists and by dermoscopic examination. Large sample size and world-wide, multicentered studies should be investigated to prove the performance of the algorithm.