CathAI:完全自动化的冠状动脉血管学解释和狭窄估计
Robert Avram1,2, Jeffrey E Olgin1,3, Zeeshan Ahmed4
1Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
NPJ digital medicine
|August 11, 2023
概括
一个新的自动化系统,CathAI,使用神经网络来高精度地解释冠状动脉疾病 (CAD) 的冠状动脉血管图. 这种方法旨在提高评估冠状动脉狭窄症的标准化和可重复性.
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:
- 冠状动脉疾病 (CAD) 的诊断严重依赖于冠状动脉血管学.
- 对狭窄血管图的视觉评估是主观和可变的.
- 在CAD评估中需要客观和可重复的方法.
研究的目的:
- 开发和验证一种完全自动化的方法,用标准冠状动脉血管图来解释冠状动脉狭窄.
- 评估自动化系统 (CathAI) 在局部化,估计和预测阻塞性CAD的性能.
- 通过内部和外部验证来评估系统的通用性.
主要方法:
- 利用来自UCSF的13843个血管图学研究进行神经网络训练.
- 开发了用于狭窄局部化和估计的顺序神经网络.
- 在保留测试数据集上进行了内部验证.
- 使用来自UOHI和MHI的数据进行了外部验证.
- 使用定量冠状动脉血管学 (QCA) 数据重新训练模型.
主要成果:
- 在现实世界的血管图表上,CathAI实现了最先进的性能.
- 高精度 (≥90% PPV,灵敏度,F1得分) 用于投影角度识别和冠状动脉检测.
- 在训练组中预测阻塞性CAD (≥70%狭窄) 的AUC为0.862.
- 在UOHI的外部验证显示,阻塞性CAD预测的AUC为0.869.
- 在MHI QCA数据上重新训练的模型实现了0.775.5的AUC.
结论:
- 可以对多个神经网络进行测序,以便对真实世界血管图进行自动分析.
- 在冠状动脉狭窄的评估中,CathAI系统显示了提高标准化和可重现性的潜力.
- 自动翻译为客观CAD诊断和管理提供了一个有前途的工具.
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