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.

Cutaneous squamous cell carcinoma (cSCC) is the second most common non‐melanoma skin cancer. Histology represents the gold standard to confirm the diagnosis of cSCC and is mandatory to determine important findings for tumour grading, such as tumour thickness, depth of invasion, degree of differentiation and histological subtype, perineural and vascular invasion, and assessing tumour margins. In daily clinical practice, the combination of clinical and histological features should be considered when grading the tumours and treating the patients, accordingly. This article aims to provide a structured overview of the most common histological findings of in situ and invasive cSCCs, namely those relevant to their severity, and should facilitate the understanding and evaluation of these results.