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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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Main Authors: Jansen, Philipp (Author) , Creosteanu, Adelaida (Author) , Matyas, Viktor (Author) , Dilling, Amrei (Author) , Pina, Ana (Author) , Saggini, Andrea (Author) , Schimming, Tobias (Author) , Landsberg, Jennifer (Author) , Burgdorf, Birte (Author) , Giaquinta, Sylvia (Author) , Müller, Hansgeorg (Author) , Emberger, Michael (Author) , Rose, Christian (Author) , Schmitz, Lutz (Author) , Géraud, Cyrill (Author) , Schadendorf, Dirk (Author) , Schaller, Jörg (Author) , Alber, Maximilian (Author) , Klauschen, Frederick (Author) , Griewank, Klaus G. (Author)
Format: Article (Journal)
Language:English
Published: 28 August 2022
In: Journal of Fungi
Year: 2022, Volume: 8, Issue: 9, Pages: 1-13
ISSN:2309-608X
DOI:10.3390/jof8090912
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3390/jof8090912
Verlag, kostenfrei, Volltext: https://www.mdpi.com/2309-608X/8/9/912
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Author Notes:Philipp Jansen, Adelaida Creosteanu, Viktor Matyas, Amrei Dilling, Ana Pina, Andrea Saggini, Tobias Schimming, Jennifer Landsberg, Birte Burgdorf, Sylvia Giaquinta, Hansgeorg Müller, Michael Emberger, Christian Rose, Lutz Schmitz, Cyrill Geraud, Dirk Schadendorf, Jörg Schaller, Maximilian Alber, Frederick Klauschen and Klaus G. Griewank
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Summary: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.
Item Description:Dieser Artikel gehört zum Special issue: Dermatophytes and dermatophytoses
Gesehen am 25.09.2023
Physical Description:Online Resource
ISSN:2309-608X
DOI:10.3390/jof8090912

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