A deep learning model for histopathological diagnosis of actinic keratosis: a diagnostic case control accuracy study
Our findings demonstrate the potential of DLMs to reliably detect AK in routine dermatopathology, suggesting their future impact as technology continues to advance.
Authors
- Georgia Gilbert (ORCID: https://orcid.org/0000-0002-3261-0001)
- Johannes Langhorst (ORCID: https://orcid.org/0009-0002-0642-1503)
- Daniel Otero Baguer (ORCID: https://orcid.org/0000-0001-6550-6043)
- Lutz Schmitz (ORCID: https://orcid.org/0000-0002-9627-2179)
- T. Schnauder
- Max Schmidt (ORCID: https://orcid.org/0000-0001-5839-1479)
- Julius Balkenhol
- Jean LE’CLERC ARRASTIA
- Francesco LACARRUBBA
- Thomas DIRSCHKA
Institutions
- Witten/Herdecke University (DE)
- University of Bremen (DE)
- Fundación Universitaria San Pablo CEU (ES)
- University of Catania (IT)
- Ruhr University Bochum (DE)
- University of Edinburgh (GB)
Publication Details
- Journal
- Italian Journal of Dermatology and Venereology
- Published
- 2026-03-01
- DOI
- https://doi.org/10.23736/s2784-8671.25.08336-7
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00