通过深度神经网络使用胸部X射线图像进行自动机会性骨质疏松症查
Jun Tang1, Xiang Yin2, Jiangyuan Lai3
1Department of Information, Daping Hospital, Army Medical University, Chongqing 400042, China.
Bone
|August 29, 2025
概括
这项研究引入了使用胸部X射线的AI驱动的骨质疏松症查工具,为DXA扫描提供了具有成本效益和无辐射的替代方案. 在早期检测骨质疏松症方面,ResNet50模型具有很高的准确性.
科学领域:
- 医学成像
- 人工智能
- 骨健康
背景情况:
- 骨质疏松症是一种常见的骨疾病,具有低骨密度,增加骨折风险.
- 目前使用双能X射线吸收计 (DXA) 的查受到成本,可访问性和辐射暴露的限制.
研究的目的:
- 开发和验证使用胸部X射线检测骨质疏松症的机会性查方法.
- 利用深度学习模型从放射图像中自动检测骨质疏松症.
主要方法:
- 分析了1995年患者的回顾性数据.
- 三个深度神经网络模型 (Inception v3,VGG16,ResNet50) 在胸部X射线上使用转移学习进行训练.
- 使用内部数据集和外部多中心验证来评估模型的性能.
主要成果:
- 在内部测试中,ResNet50模型获得了高精度 (高达90. 38%) 和AUC (高达0. 957).
- 外部验证显示 89% 的准确性和 0. 904 AUC 的强烈概括性.
- 即使同时存在肺部疾病,该模型也保持了强的性能.
结论:
- 使用胸部X射线的自动骨质疏松查方法已经开发出来.
- 这种人工智能驱动的方法为早期发现骨质疏松症提供了无辐射且具有成本效益的替代方案.
- ResNet50模型帮助临床医生及时识别和治疗骨质疏松症.
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