Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images

Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images

Blog Article

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 aluminum lotion 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 here 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.