ArticleJournal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG2026
Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata - a retrospective study.
Article in Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Artificial Intelligence in Hair Disorders: A Narrative Review.Dermatology and therapy · 2026Review
- Microbiological diagnosis of the scalp and hair in 20-40 year olds - preliminary studies.Open life sciences · 2026Review
- Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata - a retrospective study.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Article
- Review
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4 authors.
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Abstract
BACKGROUND AND
objectivesAlopecia areata (AA) is an autoimmune disease that provokes hair loss. The diagnosis is made clinically with the support of trichoscopy. However, trichoscopy requires specialized training. Deep learning models may support the diagnosis and management of AA. The aim of this study is to develop a deep learning framework to diagnose AA and to determine the AA level of activity. PATIENTS AND
methodsA retrospective analysis of trichoscopic images collected from patients with scalp diseases and healthy controls was conducted to develop a two-step deep learning framework. In Step-1, the model aimed to distinguish AA disease from both other scalp diseases and control healthy subjects. In Step-2, we intended to train a model that recognized the AA level of activity dividing the AA dataset into active, inactive, and regrowth.
resultsIn Step-1 an overall accuracy of 88.92% and an F1 score of 88.17% were achieved with an AA discriminatory capacity of 90.98%. In Step-2 an accuracy of 83.33% and an F1 score of 83.36% were reached.
conclusionsOur study highlighted for the first time the potential use of artificial intelligence in the diagnosis and staging of AA allowing more accurate diagnoses and better care.
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