人工智能增强的运动员亚临床冠状动脉疾病的检测:诊断性能和局限性
Jens Kübler1, Jan M Brendel2, Thomas Küstner2
1Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Tübingen, Germany. jens.kuebler@med.uni-tuebingen.de.
The international journal of cardiovascular imaging
|October 7, 2024
概括
基于人工智能 (AI) 的冠状动脉计算机断层扫描血管造影 (CCTA) 在无症状马拉松运动员中检测冠状动脉疾病 (CAD) 的高准确性. 虽然敏感,但人工智能需要进一步改进,以确定显著狭窄的特异性.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医疗成像医学成像
背景情况:
- 在无症状个体,特别是运动员中,冠状动脉疾病 (CAD) 查至关重要.
- 冠状动脉计算机断层扫描血管学 (CCTA) 是CAD评估的关键成像方式.
- 人工智能 (AI) 提供了增强CCTA数据分析的潜力.
研究的目的:
- 评估基于AI的CCTA用于检测CAD的诊断性能.
- 评估AI在无症状的男性马拉松运动员中使用分量流量储备 (FFR) 确定血液动力学显著狭窄的能力.
- 为了确定AI在这个人群中的准确性,敏感性,特异性和预测值.
主要方法:
- 预计招募100名45岁以上无症状的男性马拉松运动员.
- 使用AI模型 (CorEx,Spimed-AI) 的CCTA分析用于CAD检测 (≥50%狭窄,CAD-RADS 3-5) 和FFR评估 (≤0.8对>0.8).
- 对敏感性,特异性,正预测值 (PPV),负预测值 (NPV) 和准确性的统计评估.
主要成果:
- 人工智能表现出高灵敏度 (91.2%对于任何CAD,100%对于显著CAD) 和高的NPV (92.7%对于任何CAD,100%对于显著CAD).
- 对任何CAD的诊断准确率为73.4%,对显著CAD的诊断准确率为90.4%.
- 较低的PPV (25.0%对于显著的CAD) 表示虚假阳性率更高.
结论:
- 人工智能增强的CCTA是一种有前途的非侵入性工具,用于在低风险,无症状人群中检测CAD.
- 人工智能模型显示显著狭窄的高灵敏度和NPV,支持其查潜力.
- 进一步改进人工智能算法是必要的,以提高特异性和解决错误阳性,以实现最佳的临床整合.
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