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Artificial intelligence-based PRO score assessment in actinic keratoses from LC-OCT imaging using Convolutional Neural Networks.
Thamm, Janis R; Daxenberger, Fabia; Viel, Théo; Gust, Charlotte; Eijkenboom, Quirine; French, Lars E; Welzel, Julia; Sattler, Elke C; Schuh, Sandra.
  • Thamm JR; Department of Dermatology and Allergology, University Hospital, University of Augsburg, Augsburg, Germany.
  • Daxenberger F; Department of Dermatology and Allergology, University Hospital, LMU Munich, Munich, Germany.
  • Viel T; DAMAE Medical Paris, Paris, France.
  • Gust C; Department of Dermatology and Allergology, University Hospital, LMU Munich, Munich, Germany.
  • Eijkenboom Q; Department of Dermatology and Allergology, University Hospital, LMU Munich, Munich, Germany.
  • French LE; Department of Dermatology and Allergology, University Hospital, LMU Munich, Munich, Germany.
  • Welzel J; Department of Dermatology and Allergology, University Hospital, University of Augsburg, Augsburg, Germany.
  • Sattler EC; Department of Dermatology and Allergology, University Hospital, LMU Munich, Munich, Germany.
  • Schuh S; Department of Dermatology and Allergology, University Hospital, University of Augsburg, Augsburg, Germany.
J Dtsch Dermatol Ges ; 21(11): 1359-1366, 2023 11.
Article en En | MEDLINE | ID: mdl-37707430
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Queratosis Actínica Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article
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PubMed Links
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Queratosis Actínica Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article