人工智能对冠状动脉计算机断层扫描血管学进行评估,用于冠状动脉狭窄症的分类和诊断
Dan-Ying Lee1,2,3, Chun-Chin Chang1,2,3, Chieh-Fu Ko4
1Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan.
European journal of clinical investigation
|September 5, 2023
概括
一个新的深度学习 (DL) 模型在冠状动脉计算机断层扫描血管学 (CCTA) 上有效检测阻塞性冠状动脉疾病 (CAD). 这种人工智能 (AI) 工具显示出高准确性,并提高了识别CAD的工作流效率.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 使用冠状动脉计算机断层扫描血管造影 (CCTA) 诊断冠状动脉疾病 (CAD) 可能具有挑战性和耗时.
- 深度学习 (DL) 有可能简化CCTA对阻塞性CAD的解释.
研究的目的:
- 开发和验证DL模型,用于检测CCTA上的阻塞CAD.
- 评估AI模型的性能与人类读者和定量冠状动脉血管学 (QCA) 相比.
主要方法:
- 一个经过修改的Inception V3 DL模型在2929个CCTA DICOM文件中训练了7945个标签.
- 该模型将冠状动脉狭窄症分类为<50%或≥50%,由两位心脏病学家验证.
- 性能与CCTA阅读器进行了比较,使用Cohen的kappa和净重新分类指数与QCA作为参考标准.
主要成果:
- 人工智能模型在患者层面达到92.3%的准确性,在血管层面达到88.4%.
- 与CCTA读者相比,AI表现出更高的一致性 (科恩的卡帕:0.79vs.39患者;0.77vs.40血管).
- DL模型显著改善了歧视和重新分类 (净重新分类指数 = .350,p < .001).
结论:
- 开发的AI模型准确地识别了CCTA上的阻塞性CAD,与QCA相对应.
- 将这种人工智能工具集成到CCTA报告系统中,有可能增强临床工作流程.
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