一个深度学习的数字生物标志物来检测高血压,并从心电图中分层心血管风险
Mostafa A Al-Alusi1,2,3, Samuel F Friedman3,4, Shinwan Kany3,5
1Cardiology Division, Massachusetts General Hospital, Boston, USA.
NPJ digital medicine
|February 22, 2025
概括
一个新的深度学习模型HTN-AI可以使用心电图 (ECG) 波形检测高血压和心血管疾病 (CVD) 风险. 这种人工智能工具对改善高血压诊断和识别心血管疾病风险因素充满希望.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 高血压是心血管疾病 (CVD) 的重要危险因素.
- 目前的血压测量方法往往是间歇性的和低于最佳的.
- 需要改进的方法来识别高血压和评估心血管疾病风险.
研究的目的:
- 开发和验证深度学习模型 (HTN-AI) 用于高血压检测和使用12导电心电图 (ECG) 波形进行心血管疾病风险分层.
- 评估HTN-AI预测高血压概率和不良心血管结果之间的关联.
主要方法:
- 在752,415个ECG上训练了一个深度学习模型 (HTN-AI),来自103,405名成年人.
- 在56,760名成年人中进行外部验证的HTN-AI.
- 利用细灰回归来分析预测高血压概率和心血管疾病事件之间的关联.
主要成果:
- 在内部 (AUROC 0.803) 和外部 (AUROC 0.771) 验证中,HTN-AI显示了精确的高血压歧视.
- 模型预测的高血压概率与死亡,心力衰竭 (HF),心肌梗塞 (MI),中风和大动脉剖析/破裂的风险增加显著相关.
- 危险比率表明这些不良结果的风险大幅增加.
结论:
- 使用心电图数据,HTN-AI有效地识别高血压和分层心血管疾病风险.
- 该模型显示了作为高血压相关心血管疾病的数字生物标志物的潜力.
- HTN-AI可能有助于早期诊断和对心血管疾病的风险评估.
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