人工智能的诊断准确性用于在急诊室识别心脏缩和心脏缩功能障碍
Michael Gottlieb1, Evelyn Schraft1, James O'Brien1
1Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, United States of America.
The American journal of emergency medicine
|October 19, 2024
概括
人工智能 (AI) 在使用护理点超声波 (POCUS) 诊断心脏缩和扩张功能障碍方面表现出高准确性. 这种人工智能工具有望改善紧急情况下的诊断能力.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 心脏点关怀超声波 (POCUS) 通过评估缩和腹缩功能障碍,有助于急诊室 (ED) 的护理.
- 用户体验可能会限制POCUS评估的准确性.
- 人工智能 (AI) 具有提高心脏POCUS精度的潜力,但临床证据有限.
研究的目的:
- 为了确定AI的诊断准确性,用于识别心脏缩和心脏缩功能障碍.
- 为了比较AI的诊断性能与临床环境中的专家审查员.
主要方法:
- 对209名成人ED患者 (≥45岁) 进行前性观察性研究,这些患者具有心脏功能障碍的危险因素.
- 经过超声学奖学金培训的医生使用AI集成软件获得了心脏视图.
- 通过使用既定标准,定义了以射出分数 (EF) <50%和腹功能障碍为定义的缩功能障碍;AI测量了关键参数.
主要成果:
- 人工智能显示了85.7%的灵敏度和94.8%的特异性对于缩功能障碍 (LR + 16.4,LR- 0.15).
- 人工智能显示了91.9%的灵敏度和94.2%的特异性,用于扩张功能障碍 (LR+15.8,LR-0.09).
- 根据BMI对子组的分析显示了类似的结果,BMI≥30.0的敏感度略低.
结论:
- 人工智能在诊断心脏缩和放缩功能障碍时实现了高灵敏度和特异性.
- 在POCUS评估中,人工智能显示出作为一种支持专家评估的工具的巨大潜力.
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