机器学习的临床验证,护理点系统,以识别功能显著的冠状动脉疾病
Thomas D Stuckey1, Frederick J Meine2, Thomas R McMinn3
1Cone Health Heart and Vascular Center, Greensboro, NC 27401, USA.
Diagnostics (Basel, Switzerland)
|May 24, 2024
概括
一个新的机器学习算法可以准确地识别冠状动脉疾病 (CAD),而无需辐射或压力. 这种非侵入性测试显示了与CCTA相似的性能,为服务不足的人群提供了重要的诊断工具.
科学领域:
- 心脏病学 心脏病学
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 诊断在临床试验中显示出显著的性能变化.
- 冠状动脉计算机断层扫描血管造影 (CCTA) 是一种有效的排除测试,但缺乏广泛的可用性,特别是在美国农村地区.
- 农村人口缺乏医疗保健服务,需要可访问的诊断解决方案.
研究的目的:
- 为了验证先前开发的机器学习算法来识别CAD.
- 用 IDENTIFY 试验的大型盲目数据集来评估算法在冷状态中的性能.
- 为了证明在患有CAD症状的患者中,与CCTA可比的排除性能.
主要方法:
- 利用了一种机器学习的算法,需要在静止状态下获取的光聚光学和直角电压梯度信号.
- 在多中心IDENTIFY试验中的1816个未见的患者信号上验证了算法.
- 评估灵敏度,特异性和ROC-AUC,比较性能与预先指定的终点.
主要成果:
- 该算法在验证集中实现了0.80的ROC-AUC (95%CI:0.78-0.82).
- 灵敏度为0.85 (95% CI:0.82-0.88) 和特异性为0.58 (95% CI:0.54-0.62) 在预先指定的切割点.
- 在男性和女性子组中,表现一致,在4%的疾病患病率下,负预测值 (NPV) 为0.99.
结论:
- 该算法的性能与CCTA (标准的三级中心测试) 相当.
- 开发的算法可以作为CAD的非侵入性,无辐射和无压力点的护理测试.
- 这项技术有可能解决尚未满足的诊断需求,使患者,医生和医疗保健系统受益,特别是在服务不足的地区.
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