  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 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.
As part of our pipeline, we compute pairwise elastic distances between LAA
meshes from a cohort of 20 AF patients, and leverage these distances to cluster
our shape data. We demonstrate that our method clusters LAA morphologies based
on distinctive shape features, overcoming the innate inconsistencies of current
LAA categorization systems, and paving the way for improved stroke risk metrics
using objective LAA shape groups.
