深度学习算法估计胸部X射线成像的性别和年龄作为未来心血管结果的独立风险因素
Hao-Chun Liao1, Chin Lin2,3, Chih-Hung Wang4,5
1Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Republic of China.
深度学习模型可以从胸部X射线 (CXR) 预测患者的年龄,识别患心血管疾病 (CVD) 风险较高的个体. 这种基于CXR的年龄评估为风险分层提供了一个新的工具.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 心血管疾病流行病学
背景情况:
- 胸部X射线 (CXR) 含有重要的生理数据.
- 深度学习模型 (DLM) 在解释医疗图像方面取得了成功.
- 与心电图 (ECG) 年龄作为心脏生物标志物的类比表明CXR年龄潜力.
研究的目的:
- 从CXRs开发一个用于预测性别和年龄的DLM.
- 调查CXR预测年龄与未来心血管疾病 (CVD) 之间的关联.
主要方法:
- 在90,396名CXR (20-90岁) 上训练了一个DLM,以估计年龄和性别.
- 在内部和外部数据集上验证了DLM.
- 分析了CXR预测年龄和随后的健康结果之间的关系.
主要成果:
- DLM实现了平均绝对年龄预测误差为4.803 (内部) 和4.313 (外部) 年.
- 性预测AUC为0.9993 (内部) 和0.9988 (外部).
- CXR年龄比慢性年龄大5年与增加的死亡率和心血管疾病风险相关 (例如,心血管疾病死亡率HR7.57).
结论:
- DLM预测的CXR年龄为未来心血管疾病风险提供了宝贵的见解.
- CXR年龄作为临床风险分类的可访问工具.
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