评估皮肤色调尺度用于皮肤学数据集标签:一个前性比较研究
Vanessa R Weir1, Yingjoy Li1, Maura C Gillis1
1Dermatology Service, Division of Subspecialty Medicine, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
NPJ digital medicine
|December 22, 2025
概括
皮肤色调显著影响皮肤病学中的AI. 蒙克皮肤色调 (MST) 尺度可靠地评估AI数据集的皮肤色调,与菲茨帕特里克皮肤类型 (FST) 不同,提高了算法性能.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能 (AI) 在皮肤病学中的表现受皮肤色调的影响.
- 用皮肤色数据标记数据集可以增强用于检测皮肤癌的AI概括性.
- 需要对人工智能应用的皮肤色调评估方法进行大规模验证.
研究的目的:
- 评估主观 (菲茨帕特里克皮肤类型 (FST),蒙克皮肤色 (MST),潘通皮肤色指南) 和客观 (色度计) 的皮肤色评估工具的可靠性.
- 评估这些工具在亲自和基于摄影的环境中标记皮肤镜数据集的实用性.
- 确定捕捉皮肤色调变化的最有效方法,以提高皮肤病学中的AI性能.
主要方法:
- 展望观察性研究,比较面对面和基于照片的评估.
- 使用菲茨帕特里克皮肤类型 (FST),蒙克皮肤色调 (MST),潘通皮肤色调指南,以及用于皮肤色调评估的色度计.
- 与主观尺度评分和皮肤镜图像颜色值相关的色度测量 (黄金标准).
主要成果:
- 测色仪显示了高精度的人身测量.
- 蒙克皮肤色调 (MST) 在面对面和基于摄影的评估中都表现出高的重复性,与FST相比,颜色空间聚类更好.
- 皮肤镜图像颜色值与色度测量相关性不佳;MST与FST相比,更好地反映了AI黑色素瘤分类得分的差异.
结论:
- 菲茨帕特里克皮肤类型 (FST) 并不是人工智能应用中皮肤色调的可靠代理.
- 蒙克皮肤色调 (MST) 尺度是评估皮肤色调的可靠工具.
- 准确的皮肤色调评估对于改善皮肤病学AI算法的性能和通用性至关重要.
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