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用多轴视觉变压器对冠状动脉CT血管学进行CAD-RADS评分:一种临床启发的深度学习管道

Alessia Gerbasi¹, Arianna Dagliati¹, Giuseppe Albi¹

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, Italy.

Computer methods and programs in biomedicine

|December 23, 2023

概括

一个新的深度学习管道自动化冠状动脉疾病 (CAD) 评估使用冠状动脉计算机断层扫描 (CCTA) 扫描. 这种人工智能工具有助于识别需要进一步调查的患者,并对狭窄症的严重程度进行分类,提高查效率.

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:

冠状动脉疾病 (CAD) 的评估依赖于冠状动脉计算机断层扫描血管学 (CCTA).
用于CAD报告和数据系统 (CAD-RADS) 的手动CCTA分级是耗时且主观的.
边界病例在准确的CAD评估中提出了挑战.

研究的目的:

开发用于CAD选的自动化,可解释的深度学习管道.
为CCTA分析创建一个决策支持系统.
根据CAD严重程度和狭窄程度对患者进行分类.

主要方法:

使用多轴视觉转换器架构开发了一个深度学习管道.
该模型使用CCTA数据进行训练,通过处理冠状动脉截面来模拟临床实践.
为了提高预测可靠性,生成了可视解释的地图.

主要成果:

该管道实现了0.87的曲线下的面积 (AUC),用于识别需要进一步调查的患者.
在基于狭窄的基础上将患者分为CAD-RADS亚组时,获得了0.93的AUC.
该系统在253名患者的1873张CCTA图像数据库上得到了验证.

结论:

这是第一个训练有素的模型,只使用患者级数据来分配CAD-RADS分数.
该管道为CAD选提供了一种完全自动化的,可视化解释的CAD选方法.
该系统简化了CAD评估,减少了对手工分级和复杂注释的依赖.

关键词:

在CAD-RADS中,可以使用CAD-RADS.冠状动脉疾病冠状动脉疾病深度学习 (Deep Learning) 是一种深度学习.可解释的人工智能这是一个Max-ViTT.

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