Florence Decroos, Sebastian Springenberg, Tobias Lang, Marc Päpper, Antonia Zapf, Dieter Metze, Volker Steinkraus, Almut Böer-Auer
DOI: 10.2340/00015555-3893

Onychomycosis is common. Diagnosis can be confirmed by various methods; a commonly used method is the histological examination of nail clippings. A deep learning system was developed and its diagnostic accuracy compared with that of human experts. A dataset with annotations for fungal elements was used to train an artificial intelligence (AI) model. In a second dataset (n=199) the diagnostic accuracy of the AI was compared with that of dermatopathologists. The results show a non-inferiority of the deep learning system to that of analogue diagnosis (non-inferiority margin 5%) with respect to specificity and the area under the receiver operating characteristic curve (AUC). The AI achieved an AUC of 0.981. One limitation of this system is the need for a large number of training images. The AI had difficulty recognizing spores and confused serum or aggregated bacteria with fungal elements. Use of this deep learning system in dermatopathology routine might help to diagnose onychomycosis more efficiently.

Onychomycosis is a common nail infection. One diagnostic method is the histopathological examination of nail clippings, which is labour intensive. Use of artificial intelligence is emerging in medicine, but it is not yet used for the histological diagnosis of onychomycosis. A deep learning system was developed for diagnosis of onychomycosis using scanned sections of nail clippings. In 199 cases the diagnostic accuracy of the artificial intelligence was compared with that of dermatopathologists. The system can be used to assist dermatopathologists and can reduce the workload in everyday routine. Similar systems may also be developed to detect fungal organisms in skin biopsies for the diagnosis of tinea.