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三维人工智能算法整合与心内心声谱的可行性,用于在心房动导管切除过程中左心房成像

Luigi Di Biase¹, Fengwei Zou¹, Aung N Lin¹

¹Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

|July 21, 2023

概括

一个新的深度学习 (DL) 算法与心内心声谱 (ICE) 集成,准确地呈现了心房动 (AF) 的左心房解剖. 这一进步显示了通过克服操作员依赖性来改善AF除工作流的潜力.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

心脏内心回声学 (ICE) 在心房动 (AF) 废除过程中对左心房 (LA) 解剖学染是取决于操作员的.
深度学习 (DL) 图像算法有可能提高ICE的准确性和一致性.

研究的目的:

评估使用ICE的LA解剖重建的新型DL算法 (CARTOSOUND FAM模块) 的可行性和准确性.
为了比较DL算法的性能与心脏计算机断层扫描 (CT) 在接受AF切除的患者.

主要方法:

在28名AF切除患者中,使用DL算法重建了三维 (3D) LA外.
在DL-ICE和CT之间比较了LA附属体,肺静脉骨和心肌到心肌距离的测量.
独立的电生理学家对解剖学准确性进行了定性评估.

主要成果:

DL算法生成了3D LA重建,平均计算时间为65秒.
骨骨的平均直径和骨与骨之间的距离与CT相似 (r=0.52-0.75,除了RIPV r=0.20).
定性评估显示了良好的评价者之间的一致性,表明了高度的解剖学准确性.

结论:

与ICE集成的DL算法与CT和医生评估相比显示出显著的准确性.
这种ICE-DL方法的可行性可能会简化AF切除工作流程.

关键词:

人工智能的人工智能是人工智能.心房动是一种心房动.导管切除导管切除导管切除导管切除深度学习是一种深度学习.内心心电回声心脏成像在心内心电回声心脏成像

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