Abstract
BackgroundOnychomycosis 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.ObjectivesThis study evaluated the diagnostic abilities of a deep neural network (http://nail.modelderm.com) and dermoscopic examination in patients with onychomycosis.MethodsA 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.ResultsA 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).ConclusionsAs 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.
Highlights
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 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
Convolutional neural network (CNN) is a type of deep-learning algorithm that resembles the organization of the visual cortex
Summary
Convolutional neural network (CNN) is a type of deep-learning algorithm that resembles the organization of the visual cortex. Most studies have had a retrospective design and whether these data can be reproduced in a real clinical setting has not been assessed in prospective studies. Photographs are not taken for the diagnosis of onychomycosis unless it is an atypical case. Most onychomycosis images in hospital archives show atypical scenarios, introducing inherent selection bias into retrospective studies due to missing data. The study reported here collected data prospectively to reduce the risk of selection bias. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. Comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted
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