评估生物年龄:使用人工智能对心电图评估的潜力:JACC家庭系列
Francisco Lopez-Jimenez1, Suraj Kapa1, Paul A Friedman1
1Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
JACC. Clinical electrophysiology
|April 10, 2024
概括
生物年龄,比时间年龄更好地预测健康结果,可以使用人工智能 (AI) 和心电图 (ECG) 来估计. 预测年龄和实际年龄 (三角形年龄) 的差异可能表明生物衰老和生存率.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 老年学是一门学科.
背景情况:
- 生物年龄比时间年龄更准确地预测病率和死亡率.
- 人工智能 (AI) 为生物年龄估计提供了新的方法.
- 电心电图 (ECG) 是广泛可用的,非侵入性的诊断工具.
研究的目的:
- 调查人工智能训练的心电图模型在预测生物年龄方面的潜力.
- 要确定预测年龄和时间年龄 (三角形年龄) 之间的差异是否是生物年龄的有效替代品.
- 探索三角洲年龄与死亡风险之间的关系.
主要方法:
- 深度学习模型在大量ECG数据集上进行训练,以预测时间年龄.
- 计算了AI-ECG衍生年龄和时间年龄 (delta年龄) 之间的差异.
- 进行了统计分析,以评估三角形年龄与死亡率之间的关联,并对共变量进行调整.
主要成果:
- 人工智能模型在预测ECG的年龄方面取得了良好的,尽管不完美的准确性.
- 发现Delta的年龄与生存率有显著的相关性,即使经过对时间学年龄和其他死亡风险因素进行调整.
- 这表明三角洲年龄可能作为生物年龄的替代标记.
结论:
- AI-ECG分析提供了一种有希望的,非侵入性的方法来评估生物年龄.
- 来自ECG的Delta年龄可能是患病率和死亡率的有价值的预测指标.
- 将ECG与可穿戴技术相结合,可能会彻底改变生物年龄评估.
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