BRIC (BoRdeaux Institute of onCology), INSERM UMR1312, Team 5, University of Bordeaux, Bordeaux, France.
CNRS, UMR 5164, Immuno ConcEpT, University of Bordeaux, Bordeaux, France.
J Invest Dermatol. 2024 Feb;144(2):351-357.e4. doi: 10.1016/j.jid.2023.07.014. Epub 2023 Aug 15.
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.
白癜风是最常见的色素减退性皮肤疾病。鉴于新的靶向治疗方法不断发展，充分评估受累面积变得尤为重要。白癜风评分的评估可能很耗时，不同的研究者之间存在差异。因此，本研究旨在构建一种能够评估面部白癜风严重程度的人工智能系统。我们使用 100 张白癜风患者面部的图片来训练和验证人工智能模型。然后，我们使用另外 69 张面部白癜风的图片作为最终数据集。三位专家医生对 69 张面部白癜风图片进行评分。通过比较评分者和人工智能之间的评分，评估了组内和组间的表现。算法评估的准确率为 93%。总体而言，白癜风评分者和人工智能模型之间的评分具有较好的一致性。研究结果证明了该模型的潜力。它可以为面部白癜风提供客观评估，并可能成为临床实践和/或临床研究中对人类评估的补充/替代工具。
