基于深度学习的多视图面部识别作为库辛综合征的查工具
Jiaqi Qiang1, Hongjun Liu2, Xiaoyuan Guo1
1Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
概括
分析面部图像的深度学习模型可以准确诊断库辛综合征 (CS),表现优于内分泌学家. 这种人工智能方法为早期CS检测提供了一个新的工具,改善了患者的生活质量.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 内分泌学 在内分泌学.
背景情况:
- 库辛综合征 (CS) 显著影响生活质量,并且由于误诊而经常被诊断为简单的肥胖症,因此经常被诊断为晚期.
- 独特的面部特征,如月亮面,是CS的特征,表明基于图像的诊断的潜力.
研究的目的:
- 调查使用深度学习诊断库辛综合征的面部识别算法的有效性.
- 将人工智能模型的诊断性能与人类内分泌学家进行比较.
主要方法:
- 收集了42对CS患者的多视图面部图像,并与对照对照进行培训,以及13对对照进行外部验证.
- 使用五倍交叉验证训练了四个深度学习模型 (DenseNet,ResNet,Swin,ViT).
- 评估了外部验证队列的模型,并将其诊断指标与18名内分泌学家进行了比较.
主要成果:
- 四个深度学习模型在培训队列中表现稳定.
- 在外部验证队列中,模型实现了高精度 (92.3%-100%),灵敏度 (84.6%-100%),特异性 (100%) 和AUC (94.4%-100%).
- 深度学习模型的准确性超过了内分泌学家,并且与他们的敏感性和特异性相匹配或超过了它们.
结论:
- 多视图面部识别与深度学习提供了库辛综合征的准确和可访问的诊断方法.
- 人工智能模型显示出作为一种新型诊断工具的潜力,以帮助临床医生识别CS类型,超越人类专家的性能.
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