AI‐Assisted Fungal Detection in Dermatopathology: A Study on Accuracy, Efficiency, and Usability

BACKGROUND AND OBJECTIVES: AI-based image analysis is increasingly applied in pathology. Excluding fungal elements in PAS-stained skin sections is labor-intensive and well suited for AI assistance. While fungal detection in nails has been studied, skin-biopsy applications and real-world benefit remain limited. This study aimed to develop an AI algorithm for fungal detection in skin histology, create intuitive visualization software, and assess its diagnostic utility for pathologists. PATIENTS AND METHODS: A total of 466 PAS-stained cases from four institutions were digitised using three scanners. A lightweight UNet-based model was trained with semi-supervised learning and hard negative mining. Six dermatopathologists reviewed 204 independent cases in two phases: first without then with AI after a 4-week washout. Inter-observer agreement, diagnostic time, and AI-human concordance were recorded; ROC analysis assessed algorithm performance. RESULTS: The AI demonstrated strong correlation with human assessment (ROC-AUC up to 0.949). At least one pathologist agreed with AI in 91.2% of cases. AI assistance increased Fleiss' kappa from 0.67 to 0.82, raised full agreement from 64% to 78%, and halved diagnostic time, especially in negative cases. Software-usability scored 9.5/10 and integration willingness 8.3/10. CONCLUSIONS: AI assistance improved accuracy, reduced reading time, and offered user-friendly visualization, supporting integration into diagnostic workflows.