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Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images

Background: Onychomycosis 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 cas...

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Detalles Bibliográficos
Autores principales: Jansen, Philipp, Creosteanu, Adelaida, Matyas, Viktor, Dilling, Amrei, Pina, Ana, Saggini, Andrea, Schimming, Tobias, Landsberg, Jennifer, Burgdorf, Birte, Giaquinta, Sylvia, Müller, Hansgeorg, Emberger, Michael, Rose, Christian, Schmitz, Lutz, Geraud, Cyrill, Schadendorf, Dirk, Schaller, Jörg, Alber, Maximilian, Klauschen, Frederick, Griewank, Klaus G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504700/
https://www.ncbi.nlm.nih.gov/pubmed/36135637
http://dx.doi.org/10.3390/jof8090912
Descripción
Sumario:Background: Onychomycosis 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. Methods: In 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. Results: The 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%). Conclusions: Our 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.