A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists


  • Florence Decroos Dermatologikum Hamburg, Stephansplatz 5, DE-20354 Hamburg, Germany
  • Sebastian Springenberg
  • Tobias Lang
  • Marc Päpper
  • Antonia Zapf
  • Dieter Metze
  • Volker Steinkraus
  • Almut Böer-Auer




artificial intelligence, deep learning, onychomycosis, dermatopathology


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.


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How to Cite

Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V., & Böer-Auer, A. (2021). A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists. Acta Dermato-Venereologica, 101(8), adv00532. https://doi.org/10.2340/00015555-3893

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