HILLMER, Dirk; MERHI, Ribal; BONIFACE, Katia; TAIEB, Alain; BARNETCHE, Thomas; SENESCHAL, Julien; HAGEDORN, Martin

doi:10.1016/j.jid.2023.07.014

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MERHI, Ribal
Immunology from Concept and Experiments to Translation = Immunologie Conceptuelle, Expérimentale et Translationnelle [ImmunoConcept]

BONIFACE, Katia

Immunology from Concept and Experiments to Translation = Immunologie Conceptuelle, Expérimentale et Translationnelle [ImmunoConcept]

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Journal of Investigative Dermatology. 2024-02, vol. 144, n° 2, p. 351 – 357.e4

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Vitiligo is the most common depigmenting skin disorder. Given the ongoing development of new targeted therapies, it has become important to evaluate adequately the surface area involved. Assessment of vitiligo scores can be time consuming, with variations between investigators. Therefore, the aim of this study was to build an artificial intelligence system capable of assessing facial vitiligo severity. One hundred pictures of faces of patients with vitiligo were used to train and validate the artificial intelligence model. Sixty-nine additional pictures of facial vitiligo were then used as a final dataset. Three expert physicians scored the facial vitiligo on the same 69 pictures. Inter and intrarater performances were evaluated by comparing the scores between raters and artificial intelligence. Algorithm assessment achieved an accuracy of 93%. Overall, the scores reached a good agreement between vitiligo raters and the artificial intelligence model. Results demonstrate the potential of the model. It provides an objective evaluation of facial vitiligo and could become a complementary/alternative tool to human assessment in clinical practice and/or clinical research.< Réduire

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Evaluation of Facial Vitiligo Severity with a Mixed Clinical and Artificial Intelligence Approach

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