人工智能衍生型光聚缩学年龄作为心血管健康的数字生物标志物
Guangkun Nie1,2, Qinghao Zhao3, Gongzheng Tang1,2
1Institute of Medical Technology, Health Science Center of Peking University, Beijing, China.
Communications medicine
|November 19, 2025
概括
人工智能衍生型光斑质谱 (AI-PPG) 年龄估计生物年龄从PPG信号. 更大的AI-PPG年龄差距表明心血管风险更高,从而使早期干预成为可能.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 来自可穿戴设备的光电脉动扫描 (PPG) 提供了非侵入性的血液动力学监测.
- 人工智能衍生光聚体图 (AI-PPG) 年龄是一种基于深度学习的新生物年龄估计方法.
- 人工智能-PPG年龄作为心血管健康的数字生物标志物的潜力需要评估.
研究的目的:
- 引入AI-PPG年龄作为一种新的生物标志物.
- 评估AI-PPG年龄与心血管和代谢结果的关联.
- 评估AI-PPG的纵向预测值和概括性.
主要方法:
- 开发了一个具有分布意识损失函数的深度学习模型.
- 在英国生物库队列 (N=212,231) 上训练并评估模型.
- 分析了AI-PPG年龄差距与健康结果的关联,并在一个独立的队列中得到验证 (N=2343).
主要成果:
- 一个AI-PPG年龄差距>9年显著增加了主要不良心血管事件的风险 (HR=2.37,p<8.46×10−80).
- 差距小于9年与较低的风险概况有关.
- 纵向分析和外部验证证实AI-PPG年龄差异对死亡率和不良事件的预测价值.
结论:
- AI-PPG年龄是心血管健康的可扩展,非侵入性生物标志物.
- 与可穿戴设备的整合可以促进人口查和个性化监测.
- 人工智能-PPG年龄对心血管疾病的早期检测和干预充满希望.
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