Echocardiographic Framework for Left Atrial Appendage Morphology Assessment and Thromboembolic Risk Stratification in Atrial Fibrillation

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.