从胸部X射线预测骨矿物质密度及其与阻塞性睡眠呼吸暂停的相关性
Hung-Lung Tsai1,2, Kuan-Hung Cheng1, Po-Cheng Lin3
1Department of Information, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
这项研究发现,使用AI分析的胸部X射线可以帮助评估骨质疏松症风险和阻塞性睡眠呼吸暂停 (OSA) 风险. 这提供了一种新的,具有成本效益的骨健康和OSA查方法.
科学领域:
- 老年医学
- 放射学
- 医疗保健中的人工智能
背景情况:
- 人口老龄化导致慢性疾病如阻塞性睡眠呼吸暂停 (OSA) 和骨质疏松症的增加.
- 使用DXA进行传统的骨矿物密度 (BMD) 评估具有局限性,特别是在患有多系统性疾病 (如OSA) 的患者中.
- 需要创新的方法来同时评估骨健康和OSA风险.
研究的目的:
- 通过胸部X射线 (CXR) 来检测骨健康指标与阻塞性睡眠呼吸暂停 (OSA) 之间的关联.
- 用CXR数据评估人工智能 (AI) 和机器学习 (ML) 模型在预测OSA风险方面的有效性.
- 探索一种具有成本效益和无辐射的骨质疏松症和OSA查工具.
主要方法:
- 使用VeriOsteoTMOP软件从CXR图像中预测腰椎骨质量和T分数.
- 分析了70,395名接受CXR的患者的数据,根据OSA诊断和DXA结果选择了合格样本.
- 使用后勤回归,随机森林和XGBoost ML模型,结合年龄,性别,BMI和T-score等变量来预测OSA风险.
主要成果:
- 机器学习模型显示T分数 (骨健康指标) 与OSA风险之间存在显著的关联,即使在BMI控制后也是如此.
- 在一些模型的训练和验证数据集中,预测OSA风险的曲线下面面积 (AUC) 超过85%.
- 当控制BMI时,T评分对OSA风险的预测贡献是显著的.
结论:
- 基于人工智能的CXR图像分析显示了骨质疏松症查和评估OSA风险的前景.
- 将CXR与ML模型相结合,为早期检测和查提供了可行的,具有成本效益和无辐射的工具.
- 这种方法在治疗并发性OSA和骨质疏松症患者中具有显著的临床应用潜力.
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