计算机断层扫描血管学上的自动化三维左心房分析:使用深度学习管道使用八个临床相关指标的可重现性和工作流效率
Youqi Fan1,2,3, Jian Ye1,2,3, Xiaoya Wang1,2,3
1Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Frontiers in bioengineering and biotechnology
|February 2, 2026
概括
一个新的深度学习管道自动化了CT血管学 (CTA) 的左心房 (LA) 测量,显著减少了分析时间,并与专家评估有很好的一致性. 这项技术为结构性干预提供了高效和可重复的LA量化.
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
- 医学图像分析 医学图像分析
背景情况:
- 从CT血管造影 (CTA) 中精确量化左心房 (LA) 解剖学对于指导心脏干预,如切除和左心房附属物关闭 (LAAC) 至关重要.
- 手动测量LA解剖是耗时的,容易引起观察者之间的显著变化,影响程序规划和结果.
研究的目的:
- 验证基于深度学习 (DL) 的CTA管道,用于自动量化八个关键LA指标.
- 评估与专家手动测量相比,自动化DL管道的协议,可重复性和效率.
主要方法:
- 一项涉及407名患者的回顾性研究使用了基于MedNeXt的DL模型来对心脏CTA进行多结构细分.
- 一个以几何驱动的框架计算了八个关键的LA指标,包括LA和LAA体积,直径,PV轴尺寸和轴间角.
- 自动化结果与专家注释严格比较,使用类内相关系数 (ICC) 和布兰德-阿尔特曼分析.
主要成果:
- DL管道显示出与专家对主要终点的测量有很好的一致性 (LA体积ICC=0.999,直径ICCs=0.972-0.985).
- 每个案例的分析时间从15.3分钟大幅缩短到0.5分钟 (92%的减少),可用性评级高 (≥4/5) 并且手动编辑的需求最小.
- 在不同的voxel大小层中观察到一致的性能,这表明自动定量化的稳定性.
结论:
- 开发的管道提供了从CTA数据的八个LA指标的快速,可重复和专家级量化.
- 这表明将自动化系统集成到结构性干预的临床工作流程中的技术可行性.
- 建议进行进一步的前性验证,以确认这种先进的成像分析工具的程序影响.
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