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Updated: Aug 4, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
Published on: July 29, 2011
A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis
Zan Ahmad1,2, Minglang Yin2, Yashil Sukurdeep1
1Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
Left atrial appendage (LAA) shape variations influence stroke risk in atrial fibrillation (AF) patients. Our quantitative method accurately categorizes LAA morphology, improving stroke risk assessment in AF.
Area of Science:
- Cardiology
- Medical Imaging
- Computational Biology
Background:
- Left atrial appendage (LAA) morphology is linked to ischemic stroke risk in atrial fibrillation (AF) patients.
- Understanding LAA morphology can improve stroke risk stratification and elucidate AF-related stroke mechanisms.
- Current qualitative LAA descriptions are inconsistent and hinder research progress.
Conclusions:
- A quantitative pipeline combining elastic shape analysis and unsupervised learning effectively categorizes LAA morphology in AF patients.
- This method offers a reliable approach to overcome the inconsistencies of qualitative LAA descriptions.
- The findings pave the way for improved stroke risk stratification tools in AF.
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