A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists
Open accessAbstract
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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The AI model for onychomycosis could offer a promising approach for preliminary screening, highlighting regions of interest on WSIs for further evaluation and confirmation by dermatopathologists.
“Furthermore, this AI model could offer a promising approach for the preliminary screening of onychomycosis, highlighting regions of interest on WSIs for further evaluation and confirmation by dermatopathologists [33,34].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [34]
Decroos et al.'s AI-based approach for detection of fungal hyphae in PAS-stained WSIs of nail clippings was non-inferior to four dermatopathologists with different levels of experience with regard to specificity and accuracy, but more uncertain in regard to sensitivity.
“The results showed that AI was non-inferior to four dermatopathologists (with different levels of experience) with regard to specificity and accuracy, but more uncertain in regard to sensitivity.”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [34]
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“Furthermore, this AI model could offer a promising approach for the preliminary screening of onychomycosis, highlighting regions of interest on WSIs for further evaluation and confirmation by dermatopathologists [33,34].”
- Authors
- Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A.
- Affiliations
- Dermatologikum Hamburg (DE)
- University of Münster (DE)
- Corresponding author
- Decroos, F
- Venue
- Acta Dermato Venereologica
- Publisher
- Medical Journals Sweden AB
- Published
- 2021
- Volume
- 101
- Issue
- 8
- Pages
- adv00532
- ISSN
- 0001-5555 (ISSN-L)
- 1651-2057
- PMC
- PMC9413660
- OpenAlex
- W3196142250
- Cited by
- 51
- References
- 32
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 10 · 2023: 7 · 2022: 7 · 2021: 1
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Acta Dermato Venereologica (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Nail Diseases and Treatments
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- onychomycosis, deep learning, histopathological diagnosis, nail clippings, dermatopathology, fungal elements, diagnostic accuracy, non-inferiority
- MeSH terms
- Deep Learning, Artificial Intelligence, Humans, ROC Curve, Onychomycosis
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