ArticleDermatology practical & conceptual2026
Dermoscopic Features of Onychomycosis Identified by Image Analysis Using a Decision Tree Algorithm.
Article in Dermatology practical & conceptual, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionOnychomycosis is a common fungal nail infection characterized by diverse dermoscopic patterns that vary depending on clinical and demographic factors. Accurate diagnosis is critical to differentiate onychomycosis from other nail conditions. Traditional diagnostic methods such as fungal culture are time-consuming and cost-effective, highlighting the need for innovative approaches.
objectiveThis study aimed to describe and analyze the dermoscopic features of onychomycosis and to investigate associations between specific onychomycosis patterns and various patient-related factors using machine learning techniques to enhance diagnostic accuracy and improve treatment strategies. METHODOLOGY: A total of 130 patients diagnosed with onychomycosis were prospectively enrolled. Dermoscopy served as a noninvasive tool for classifying nail patterns accurately. Decision tree analysis was applied to examine relationships between nail condition patterns and clinical variables such as age, duration of the disease, diabetes status, and occupation.
resultsThe main dermoscopic features of onychomycosis were longitudinal striae, ruin appearance, aurora, spikes, jagged borders, and distal pulverized edges. Notably, spikes and jagged borders were identified in 70% of cases, strongly correlating with onychomycosis and highlighting their diagnostic relevance. Furthermore, analysis indicated that demographic and clinical factors significantly influence specific nail disorder manifestations.
conclusionThe study revealed significant dermoscopic signs, which may serve as essential markers for identifying onychomycosis. In addition, the study explored essential relationships between nail condition patterns and various patient-related factors. Despite limitations from the small dataset, this study establishes a promising foundation for future work to confirm and expand upon these findings, further integrating machine learning in dermatological diagnostics.
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