基于深度学习的PET-CT冠状动脉评分的可行性和局限性:与CTCT冠状动脉评分进行比较
Hee Sang Oh1, Tae Hoon Kim1, Ji Won Kim1
1Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonjuro, Gangnam-Gu, Seoul, 06273, Republic of Korea.
基于深度学习的冠状动脉评分使用正子发射断层扫描-计算机断层扫描 (PET-CT) 显示了与传统心脏计算机断层扫描 (CaCT) 相似的结果. 然而,在PET-CT中潜在的低估和高假阴性率需要仔细考虑冠状动脉疾病风险评估.
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
- 放射学 放射学是一门学科.
背景情况:
- 冠状动脉疾病 (CAD) 风险分层至关重要.
- 冠状动脉评分 (CACS) 使用心电图带有非对比增强的心脏计算断层扫描 (CaCT) 是一种标准方法.
- 电脑断层扫描 (PET-CT) 越来越多地被使用,特别是在易患CAD的癌症患者中.
研究的目的:
- 评估基于深度学习的CACS使用PET-CT的可行性和局限性.
- 将人工智能辅助的PET-CT CACS与传统的CaCT CACS进行比较.
- 评估AI软件对CACS测量的影响.
主要方法:
- 对215名接受了CaCT和PET-CT的个人进行了回顾性分析.
- 使用Agatston方法计算CaCT,基于阅读器的PET-CT和基于AI的PET-CT (PET-CT(AI)) 的CACS.
- 总CACS和指南分类的CACS在模式之间进行比较,分析差异,相关系数,类内系数 (ICC) 和一致率.
主要成果:
- 在CaCT和PET-CT之间有很高的相关性 (r=0.768) 和ICC (0.842) CACS.
- 在CaCT和PET-CT (AI) 之间,一致率为73.8%,假阴性率为37.3%,假阳性率为4.4%.
- 错误分类率的增加与年龄较大和男性性别有关.
结论:
- 在PET-CT上使用人工智能辅助的CACS显示了与CaCT相似的性能.
- 低估的趋势和PET-CT CACS中显著的虚假负率需要谨慎.
- 在PET-CT上自动化的CACS可以为接受常规扫描的癌症患者的CAD风险评估提供一个有价值的工具.
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