自动化和可解释的冠状动脉血管图解释用于选择冠状动脉旁路移植候选人,使用人工智能
Tom X Liu1, Patrick M McCarthy1, Adwaiy Manerikar1
1Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, IL; Division of Cardiac Surgery, Northwestern University Feinberg School of Medicine and Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, IL.
The Journal of thoracic and cardiovascular surgery
|August 16, 2025
概括
自动计算机视觉模型可以从冠状动脉血管图中识别冠状动脉旁路手术的候选人,从而有可能提高心血管护理的准确性和质量控制.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 从血管造影中对冠状动脉狭窄的视觉解释容易出现人为错误.
- 机器学习模型提供了提高准确性的潜力,但往往缺乏可解释性.
- 人工智能在心脏病学中的临床应用受到透明和可靠工具的需求的阻碍.
研究的目的:
- 开发和评估一种自动化计算机视觉模型,用于识别冠状动脉旁路移植 (CABG) 的候选人.
- 评估模型在检测冠状动脉病变和根据既定指南确定CABG资格方面的准确性.
- 为解释冠状动脉血管图提供一种更客观,更有效的方法.
主要方法:
- 在2018-2023年期间对初级CABG的医疗记录进行查,以获得冠状动脉血管图视频和报告.
- 开发一种计算机视觉算法来分析血管图像视频片段并识别狭窄性病变.
- 将自动化模型推与临床报告和指导方针 (AHA/ACC 2021) 对 CABG 指示进行比较.
主要成果:
- 该模型分析了来自349名患者的4,472段视频,在每项研究平均51.1秒的时间内识别了682个病变.
- 该算法在识别CABG候选人的过程中显示了整体准确度为74%,正预测值为61%,负预测值为87%.
- 具体的错误包括错误分类正常血管图 (6%) 和预测皮肤间干预而不是绕道由于未被认可的左主或环损伤.
结论:
- 对CABG候选人的自动识别显示了增强冠状动脉血管图的视觉解释的希望.
- 未来的改进可以加强质量控制,并在临床实践中支持指导方针导向的重血管化策略.
- 这项技术有可能提高心血管诊断解释的一致性和效率.
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