使用人工智能启用智能手表电心图的血清水平监测
I-Min Chiu1, Po-Jung Wu2, Huan Zhang3
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Emergency Medicine, Chang Gung Memorial Hospital Kaohsiung Branch, Kaohsiung, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
JACC. Clinical electrophysiology
|October 10, 2024
概括
一种AI-ECG算法Kardio-Net准确地预测了使用智能手表ECG的末期病 (ESRD) 患者的血清水平. 这项技术可以远程监测高血症,降低与水平升高相关的风险.
科学领域:
- 人工智能在医学中的应用
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 可穿戴技术可穿戴技术
背景情况:
- 高血症 (血清含量升高) 会增加心脏突然死亡的风险,特别是在慢性病和末期病 (ESRD) 患者中.
- 传统的监控方法具有侵入性,资源密集性,缺乏连续跟踪能力.
- 智能手表ECG提供了与AI解释的个性化远程监控的潜力.
研究的目的:
- 开发和验证AI-ECG算法,用于预测ESRD患者的血清水平.
- 通过智能手表产生的心电图波形来评估算法的性能.
主要方法:
- 在超过15万个心电图和配对的血清水平上训练了一种AI-ECG模型 ("心电网").
- 我们对1463名ESRD患者的4337个心电图进行了微调,使用了12导和单导心电图输入.
- 在Cedars Sinai,斯坦福医疗保健和潜在的国际智能手表ECG队列的独立队列上验证了该模型.
主要成果:
- 卡迪奥网在预测严重高血症 (>6.5mEq/L) 中取得了高准确性,AUC在不同心电图类型和验证集中从0.807到0.876不等.
- 预测水平的平均绝对误差 (MAE) 在0.527至0.740mEq/L之间.
- 该模型在外部验证数据集 (包括智能手表衍生的ECG) 上表现一致.
结论:
- 一种深度学习模型 (Kardio-Net) 从12导电心电图和单导电智能手表数据中有效预测血清水平.
- 经过验证的模型显示,在ESRD患者中远程监测高血症的显著实用性.
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