人工智能用于直接向医生报告门诊心电图
L S Johnson1,2, P Zadrozniak3, G Jasina3
1Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden. linda.johnson@med.lu.se.
Nature medicine
|February 10, 2025
概括
与人类技术人员相比,人工智能 (AI) 模型DeepRhythmAI在门诊心电图 (ECG) 分析中显著减少了错过的关键心律不整. 虽然人工智能增加了假阳性,但它大大提高了紧急心脏病的诊断准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗技术 医疗技术 医学技术
背景情况:
- 健行心电图 (ECG) 技术产生了大量的数据集,需要专家解释.
- 目前的解释依赖于人类技术人员,这可能会造成延误和错误.
- 人工智能 (AI) 为自动ECG分析提供了一个潜在的解决方案.
研究的目的:
- 评估人工智能算法 (DeepRhythmAI) 的性能,用于直接向医生报告门诊心电图.
- 为了比较人工智能的诊断准确度与人类技术人员在识别关键心律失常时的诊断准确度.
- 评估人工智能对减少误报和误报结果的影响.
主要方法:
- 通过167名认证技术人员和DeepRhythmAI模型对14606个门诊心电图记录进行节拍注释.
- 17个心脏病学家共识小组对5,235个节律事件的随机样本进行了审查.
- 性能指标包括灵敏度,假负率和假阳性率进行了比较.
主要成果:
- 与技术人员 (80.3%) 相比,DeepRhythmAI对临界心律失常的敏感性 (98.6%) 显著更高.
- 人工智能模型的假阴性率 (3.2/1,000患者) 与技术人员 (44.3/1,000患者) 相比显著较低,减少了错误诊断的14.1倍.
- 人工智能模型的假阳性率较高 (12/1,000患者日),与技术人员 (5/1,000患者日) 相比.
结论:
- DeepRhythmAI模型显示了临界心律失常的优异负预测值,显著减少了错过诊断.
- 用人工智能进行直接向医生报告的分析,有可能降低医疗保健成本,改善患者的获取和结果.
- 虽然人工智能增加了假阳性,但它减少假阴性的能力在门诊心电图监测中提供了显著的临床优势.
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