人工智能成像模型在检测皮肤病的表现方面表现出更高的菲茨帕特里克皮肤颜色分类
1College of Medicine, University of Cincinnati, Cincinnati, OH, United States.
JMIR dermatology
|August 26, 2023
概括
深度学习模型难以诊断黑色肤色的皮肤状况 (菲茨帕特里克类型 4-5). 人工智能需要更多多样化的培训数据来准确识别所有皮肤类型的黑色素瘤和基底细胞癌.
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:
- 深度学习模型在较深色肤色 (菲茨帕特里克皮肤类型 4-5) 的图像上表现不佳.
- 这种差异影响了不同人群中皮肤病的准确诊断.
研究的目的:
- 评估图像识别模型的性能差异,区分黑色素瘤和基底细胞癌在肤色较暗 (菲茨帕特里克4-5型) 和肤色较浅 (菲茨帕特里克1-3型) 的人群中.
- 评估模型在不同皮肤类型的相同数量的图像上训练时的性能是否有所不同.
主要方法:
- 开发了两个图像识别模型,以区分黑色素瘤和基底细胞癌.
- 模型经过训练,验证和测试,使用来自多个皮肤病 atlases 的开源图像数据集.
主要成果:
- 在较浅的皮肤图像上训练的模型表现出卓越的灵敏度,特异性,正预测值,负预测值和F1分数.
- 当模型被训练并通过使用较深色肤色图像进行验证时,表现较低.
结论:
- 当前的人工智能模型需要从肤色较暗的个体获得更多样化的图像数据,以实现公平的诊断性能.
- 从Fitzpatrick皮肤类型4-5增加皮肤学图像的数量对于改善不同人群的AI准确性至关重要.
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