Ahmad Zan, Yin Minglang, Sukurdeep Yashil, Rotenberg Noam, Kholmovksi Eugene, Trayanova Natalia
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
ArXiv. 2024 Nov 24:arXiv:2403.08685v3.
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.
左心耳（LAA）的形态学变异与心房颤动（AF）患者不同水平的缺血性中风风险相关。研究LAA形态可以阐明这种关联背后的机制，并推动先进的中风风险分层工具的开发。然而，目前对LAA形态的分类描述本质上是定性的，且各研究之间不一致，这阻碍了我们对AF中风发病机制理解的进展。为了缓解这些问题，我们引入了一种定量流程，该流程将弹性形状分析与无监督学习相结合，用于对AF患者的LAA形态进行分类。我们证明，我们的方法能够根据LAA的几何特征可靠地对其进行聚类，从而为克服当前定性LAA分类系统的局限性提供了一条途径。
