AI powered Detection and Assessment of Onychomycosis: A Spotlight on Yellow and Deep Learning

Despite significant advances in computer-aided diagnostics, onychomycosis, a widespread fungal nail infection, lacks an automated approach for analysis and classification. The primary aim of our study was to develop and validate automated machine learning models for the accurate detection and classification of onychomycosis-aNected areas in toenails. The images used in this study were acquired using Scarletred®Vision mobile App under ambient light conditions, and all have uniform dimensions. Considering a total of 1687 unique images from 440 subjects, the research explores various degrees of onychomycosis and evaluates the extent of the infection in the detected toenails. We developed an advanced machine learning algorithm for precise segmentation and classification of onychomycosis-aNected toenails, utilizing expert annotations and advanced post-processing techniques. Additionally, an analysis of nail growth was performed, and a comparison graph with the percentage of infection was estimated. Using advanced machine learning algorithms, we successfully detected toenails, allowing for a detailed analysis of intricate structures within the images. We achieved a final validation loss of 0.0236 and an F1 score of 0.8566 for accurate toenail detection, while the Random Forest algorithm demonstrated 81% accuracy in classifying and distinguishing between infected and healthy areas of toenails aNected by onychomycosis. The superpixel method improved the algorithm's precision in identifying the infected regions, oNering promising advancements for decision support. Limited number of patients with darker skin color which may impact in the performance of the AI. While primarily examining the big toe’s toenail, our approach holds promise for extending and validating on a complete dataset, allowing broader assessments for onychomycosis. Our advanced machine learning algorithm shows enhancement in expert decision making.