Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation.
- 1Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai 602105, India.
- 2Department of Computer Science Engineering, Sriram Engineering College, Chennai 602024, India.
Abstract
Background: Left Atrial Appendage (LAA) causes a thrombus risk in patients with atrial fibrillation (AF). Accurate identification of LAA morphology reduces thrombotic stroke. Methods: This study proposes a hybrid deep learning framework, Left Atrial Appendage Thrombus Framework (LAATF), which integrates Adaptive CLAHE, optimized Chan-Vese contour extraction, and a multi-headed AgileFormer transformer to classify LAA morphology, detect thrombus, and estimate image-derived thromboembolic risk using the LightGBM-based proposed Thromboembolic Risk index (TRI) score. A dataset of 4320 temporal images from 240 patients with AF was used, comprising four LAA morphologies (chicken-wing, windsock, cactus, and cauliflower). CLAHE enhanced local contrast. The Chan-Vese model delineated appendage contours. AgileFormer extracted image-derived textural and morphological features to classify morphology and thrombus presence. LightGBM-based TRI regression estimated thromboembolic risk in AF patients with LAA. Results: The proposed framework achieved a morphology classification accuracy of 91-94% and thrombus detection AUC of 0.935, with a sensitivity of 93.4% and specificity of 92.6%. The computed TRI correlated strongly with actual outcomes (R² = 0.98, r = 0.99). Among morphologies, cauliflower and cactus types show the highest thromboembolic risk due to their increased texture entropy and image-based features. Conclusions: The LAATF framework provides an interpretable, image-based technique for LAA morphology assessment and thrombus prediction.
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BibTeX
@article{jayakrishnan2026echocardiographic,
title = {{Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation.}},
author = {Subramanian Jayakrishnan and Jeyakumar Anish Kumar and Nagalingam Rajendiran Shanker},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/diagnostics16193154},
url = {https://europepmc.org/article/MED/42882310},
note = {CC BY},
}
RIS
TY - JOUR
TI - Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation.
AU - Subramanian Jayakrishnan
AU - Jeyakumar Anish Kumar
AU - Nagalingam Rajendiran Shanker
PY - 2026
DA - 2026/09/28
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193154
UR - https://europepmc.org/article/MED/42882310
AB - Background: Left Atrial Appendage (LAA) causes a thrombus risk in patients with atrial fibrillation (AF). Accurate identification of LAA morphology reduces thrombotic stroke. Methods: This study proposes a hybrid deep learning framework, Left Atrial Appendage Thrombus Framework (LAATF), which integrates Adaptive CLAHE, optimized Chan-Vese contour extraction, and a multi-headed AgileFormer transformer to classify LAA morphology, detect thrombus, and estimate image-derived thromboembolic risk using the LightGBM-based proposed Thromboembolic Risk index (TRI) score. A dataset of 4320 temporal images from 240 patients with AF was used, comprising four LAA morphologies (chicken-wing, windsock, cactus, and cauliflower). CLAHE enhanced local contrast. The Chan-Vese model delineated appendage contours. AgileFormer extracted image-derived textural and morphological features to classify morphology and thrombus presence. LightGBM-based TRI regression estimated thromboembolic risk in AF patients with LAA. Results: The proposed framework achieved a morphology classification accuracy of 91-94% and thrombus detection AUC of 0.935, with a sensitivity of 93.4% and specificity of 92.6%. The computed TRI correlated strongly with actual outcomes (R² = 0.98, r = 0.99). Among morphologies, cauliflower and cactus types show the highest thromboembolic risk due to their increased texture entropy and image-based features. Conclusions: The LAATF framework provides an interpretable, image-based technique for LAA morphology assessment and thrombus prediction.
SN - 2075-4418
ER -