对心房颤动患者左房阑尾几何形状进行聚类的弹性形状分析计算。

ArXiv Pub Date : 2024-11-24
Zan Ahmad, Minglang Yin, Yashil Sukurdeep, Noam Rotenberg, Eugene Kholmovksi, Natalia Trayanova
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引用次数: 0

摘要

左心房阑尾(LAA)形态的变化与心房颤动(AF)患者不同程度的缺血性中风风险有关。研究 LAA 形态学可以阐明这种关联背后的机制,从而开发出先进的卒中风险分层工具。然而,目前对 LAA 形态的分类描述是定性的,而且不同研究之间也不一致,这阻碍了我们对房颤卒中发病机制的进一步了解。为了缓解这些问题,我们引入了一个定量管道,将弹性形状分析与无监督学习相结合,对房颤患者的 LAA 形态进行分类。作为管道的一部分,我们计算了来自 20 名房颤患者的 LAA 网片之间的成对弹性距离,并利用这些距离对形状数据进行聚类。我们证明了我们的方法能根据独特的形状特征对 LAA 形态进行聚类,克服了当前 LAA 分类系统的先天不一致性,并为利用客观 LAA 形态组改善中风风险度量铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Elastic shape analysis for unsupervised clustering of left atrial appendage morphology.

Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the development of advanced stroke risk stratification tools. However, current categorical descriptions of LAA morphologies are qualitative in nature, and inconsistent across studies, which impedes advancements in our understanding of stroke pathogenesis in AF. To mitigate these issues, we introduce a quantitative pipeline that combines elastic shape analysis with unsupervised learning for the categorization of LAA morphology in AF patients. We demonstrate that our method reliably clusters LAAs based on their geometric features, and thus provides an avenue to overcome the limitations of current qualitative LAA categorization systems.

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