人工智能使用心电图数据预测年龄:探索生物年龄差异
Shaun Evans1, Sarah A Howson2, Andrew E C Booth1
1Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia.
Heart rhythm
|September 28, 2024
概括
人工智能 (AI) 可以从心电图 (ECG) 预测生物年龄. 这种AI-ECG模型显示女性和老年人的生物学年龄较小,年轻患者的年龄更高.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 在心电图 (ECG) 上训练的人工智能 (AI) 模型可以预测生物年龄.
- 来自ECG的生物年龄预测是死亡率和心血管事件的预后指标.
研究的目的:
- 开发一种人工智能模型,用ECG来预测生物年龄.
- 为了比较基线特征并确定高级生物年龄的决定因素.
主要方法:
- 从63,246名心脏病住院患者 (20至90岁) 的心电图上训练了一个卷积神经网络AI模型.
- 在内部验证模型 (80:20分) 和外部使用英国生物银行数据.
- 使用相关性,差异和平均绝对差异指标分析性能.
主要成果:
- 在内部验证中,AI-ECG模型实现了0.72的相关系数,平均绝对年龄差异为9.1年.
- 外部验证显示了类似的性能.
- 观察到显著的分组差异:年轻患者 (20-29岁) 的生物年龄较高 (14.3岁),而老年患者 (80-89岁) 的生物年龄较低 (10.5岁).
- 女性和患有单个心电图的患者分别比男性和患有多个心电图的患者生物学上更年轻.
结论:
- 人工智能-心电图预测的生物年龄显示了患者子组之间的显著差异.
- 生物学年龄偏离时间学年龄,特别是在年轻的住院患者 (较年长的生物学年龄) 和较年长的住院患者 (较年轻的生物学年龄) 中.
- 性别和记录的心电图数量与生物年龄的差异有关.
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