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Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images

Authors :
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
Klaus G. Griewank
Source :
Journal of Fungi, Vol 8, Iss 9, p 912 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
2309608X
Volume :
8
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Journal of Fungi
Publication Type :
Academic Journal
Accession number :
edsdoj.3d640e89532b47a29f9d608d3ff8bc98
Document Type :
article
Full Text :
https://doi.org/10.3390/jof8090912