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人工智能预测年龄的回声心脏图像作为心血管疾病的标志物
Meenal Rawlani1, Hirotaka Ieki2, Christina Binder1
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
NPJ digital medicine
|November 18, 2025
概括
这项研究开发了一种深度学习模型,从心声回声图视频中预测生物年龄. 该模型准确估计了心血管衰老,并确定了心脏病和中风的风险.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 年代年龄是疾病的普遍风险因素.
- 评估生物年龄可能会改善心血管风险预测.
- 通过比较生物年龄和时间年龄,可以识别加速或延迟的心血管衰老.
研究的目的:
- 开发和验证一种深度学习模型,用于从心声回声图视频中预测年龄.
- 评估模型在不同患者队伍中的表现.
- 调查预测生物年龄与心血管疾病风险的关联.
主要方法:
- 在2,610,266张来自90,738名患者的心声回声录像视频上训练了一种深度学习模型.
- 该模型使用了多视图心声回声学数据.
- 使用平均绝对误差 (MAE) 和确定系数 (R2) 评估性能,并对四个队列进行外部验证.
主要成果:
- 该模型在测试组件上实现了6.76年的MAE和0.732的R2.
- 在外部验证队列中观察到一致的表现.
- 预测的年龄与冠状动脉疾病,心力衰竭和中风的风险增加有关,并反映了心脏移植周围的变化.
结论:
- 深度学习模型可以准确地从心声回声图中预测年龄,提供对心血管衰老的见解.
- 这种方法有可能提高心血管风险评估.
- 对心声回声图的计算机视觉分析可能为衰老和疾病风险提供新的生物标志物.
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