人工智能支持的心电图分析提高了诊断准确度,减少了错误的STEMI激活:一个多中心的美国. 登记处 登记处 登记处 登记处
Robert Herman1, Bryn E Mumma2, Jake D Hoyne3
1Cardiovascular Center Aalst, AZORG, Aalst, Belgium; Powerful Medical, Bratislava, Slovakia.
JACC. Cardiovascular interventions
|October 29, 2025
概括
人工智能 (AI) 电图分析显著改善了ST段升高心肌梗塞 (STEMI) 检测,并减少了虚假激活. 这种人工智能工具增强了不典型的STEMI表现的识别,支持其整合到急性胸痛协议中.
科学领域:
- 心脏病学 心脏病学
- 医疗人工智能 医疗人工智能
- 医疗信息学 医疗信息学
背景情况:
- 及时转血对于降低ST段升高心肌梗塞 (STEMI) 的死亡率至关重要.
- 目前的心电图导向心脏导管实验室 (CCL) 激活改善了反应,但面临诊断的不确定性,导致假阳性激活 (FPA) 和延迟,特别是异常呈现.
研究的目的:
- 评估基于AI的ECG分析在现实世界STEMI选中的诊断性能.
- 评估人工智能在多中心STEMI诊断中的运营影响.
主要方法:
- 在美国三个PCI中心 (2020年1月至2024年5月) 进行了对1032名疑似STEMI患者进行新兴CCL激活的回顾性分析.
- 标准分组与盲目的回顾性AI心电图分析 (心脏女王,PMcardio) 的比较,用于检测急性冠状动脉封闭和模仿.
- 参考标准:通过阳性酶进行血管学确认的罪祸首病变. 分析包括诊断准确性,子组表现和FPA重新分类.
主要成果:
- 人工智能心电图分析显示STEMI检测的优异灵敏度 (92.0%) 与标准分拣 (71.0%) 相比 (p < 0.001).
- 人工智能显著降低了虚假阳性激活率 (7.9%与41.8%) 和提高了特异性 (81.0%与29.0%) (p < 0.001).
- 人工智能实现了0.94的AUC,在具有挑战性的子组中保持了表现,并正确重新分类了91%的生物标志物阴性FPAs.
结论:
- 基于人工智能的心电图分析大大提高了STEMI检测的准确性,并减少了不必要的干预.
- 人工智能模型有效地识别了非传统的STEMI表现,解决了诊断的不确定性.
- 研究结果支持将AI-ECG分析整合到急性胸痛管理途径中,以改善患者的治疗结果.
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