自动冠状动脉检测和评分在多中心,多协议非对比CTCT上
Andrew M Nguyen1, Jianfei Liu1, Tejas Sudharshan Mathai1
1National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Bethesda, Maryland, United States.
Journal of medical imaging (Bellingham, Wash.)
|November 17, 2025
概括
一个深度学习模型在CT扫描上准确地检测和评分冠状动脉化斑块,改善心血管风险评估. 这种自动化方法有助于早期检测和分层患者患心脏病风险.
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:
- 冠状动脉疾病 (CAD) 是全球首要的死亡原因.
- 准确评估冠状动脉化斑块对于心血管风险分层至关重要.
- 目前用于斑块检测和评分的方法可能是劳动密集型和主观的.
研究的目的:
- 开发和验证一种深度学习方法,用于在非对比CT扫描上自动检测和评分冠状动脉化斑块.
- 通过自动化斑块分析,提高心血管风险评估的准确性和效率.
主要方法:
- 使用了五个数据集 (1个内部,4个外部),包括641次培训和160次测试非对比CT扫描.
- 使用nnU-Net框架开发了一个深度学习模型,同时对主动脉,心脏和肺部进行细分.
- 自动计算Agatston分数来量化斑块负担,并与以前的方法进行比较.
主要成果:
- 该模型在预测和参考Agatston分数之间实现了强烈的相关性 (r2=0.973).
- 展示了高性能,精度为89.3%,回忆率为89.1%,子得分为75.0%.
- 达到了92.0%的准确性和科恩的卡帕为0.913的Agatston组分层,与临床结果相关.
结论:
- 拟议的基于nnU-Net的深度学习方法在非对比CT扫描上准确地检测和分割冠状动脉斑块.
- 自动化Agatston分数计算可实现有效的心血管风险分层.
- 这种方法促进了机会性查和大规模的基于人群的CAD研究.
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