通过人工智能增强的心电图分析改变动脉样性心血管疾病的健康查:机遇和挑战
Dhruva Biswas1,2, Arya Aminorroaya1,2, Philip M Croon1,2
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Current atherosclerosis reports
|September 1, 2025
概括
人工智能增强心电图 (AI-ECG) 对动脉样硬化心血管疾病 (ASCVD) 的查有前途,改善了风险预测和早期检测. 进一步对不同群体的验证和解决实施挑战对于广泛的临床采用至关重要.
科学领域:
- 心脏病学
- 人工智能
- 公共卫生
背景情况:
- 动脉样性心血管疾病 (ASCVD) 仍然是全球主要的死亡原因.
- 传统的风险分层模型在识别所有有风险的个体方面存在局限性.
- 电心电图 (ECG) 是一种广泛使用且廉价的诊断工具.
研究的目的:
- 审查人工智能增强型心电图 (AI-ECG) 在人口层面的ASCVD查中的新兴作用.
- 评估AI-ECG在预测主要不良心血管事件和检测亚临床冠状动脉疾病方面的能力.
- 确定AI-ECG在ASCVD查中的临床,方法和实施挑战.
主要方法:
- 对心血管疾病中AI-ECG应用的综合文献综述.
- 在预测ASCVD事件和亚临床疾病方面分析AI-ECG模型的性能.
- 评估AI-ECG模型的当前验证状态,偏差和通用性.
主要成果:
- 最先进的AI-ECG模型显示出高精度,在重新分类患者方面表现优于传统风险模型.
- AI-ECG可以缩短心血管事件的预测时间,使得早期干预成为可能.
- 目前的验证主要是以回顾性和医院为基础的,由于偏见,其概括性有限.
结论:
- AI-ECG有可能通过将心电图转化为强大的预测工具来彻底改变ASCVD查.
- 需要进行前性研究来证实AI-ECG对不同人群的治疗结果的影响.
- 应对后勤挑战和建立信任对于成功的临床实施和全球影响至关重要.
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