ArticleJournal of fungi (Basel, Switzerland)2022
Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.
Article in Journal of fungi (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Artificial Intelligence in the Diagnosis of Superficial Mycoses: A Scoping Review.Journal of fungi (Basel, Switzerland) · 2026Review
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- Human expertise or artificial intelligence? A prospective study on nail disorder diagnosis.NPJ digital medicine · 2026Article
- Artifact-Aware Fungal Detection in Dermatophytosis: A Transformer-Based Approach for KOH Microscopy.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Enhancing nail disease diagnosis: a capsule network with SE attention and dual backbone models.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Review
- Low resource federated learning for classification of nail disease by deploying cross-silo and heterogeneously dataset distributions.Scientific reports · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- The structural language of fungal cell wall sugars in WHO priority fungi: the determinant of pathogenicity and the gateway to new therapies.Frontiers in immunology · 2026Review
- Microbial evaluation of Melaleuca alternifolia essential oil for its antifungal activity against Trichophyton violaceum and its synergistic effects with ITZ and KTZ.Archives of dermatological research · 2025Article
- Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?Clinical, cosmetic and investigational dermatology · 2025Review
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis.BMC medical informatics and decision making · 2024Article
- Artificial Intelligence in the Diagnosis of Onychomycosis-Literature Review.Journal of fungi (Basel, Switzerland) · 2024Review
- The Promising Role of Artificial Intelligence in Nail DiseasesBalkan medical journal · 2024Article
- Diagnosis of invasive fungal infections: challenges and recent developments.Journal of biomedical science · 2023Review
- Classification of melanonychia, Beau's lines, and nail clubbing based on nail images and transfer learning techniques.PeerJ. Computer science · 2023Article
- Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review.Indian dermatology online journalArticle
Corrections and comments
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Authors and funding
20 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOnychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists.
methodsIn total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides.
resultsThe U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%).
conclusionsOur results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.
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