PREDICTIVE MODELING OF CARDIOVASCULAR EVENTS USING AI-ENHANCED ECHOCARDIOGRAPHIC FEATURE EXTRACTION

Abstract

Accurate prediction of major cardiovascular events remains a critical priority in preventive cardiology, yet traditional echocardiographic interpretation is limited by operator subjectivity and insufficient sensitivity to subtle myocardial abnormalities. This study presents an AI-enhanced predictive modeling framework that leverages advanced echocardiographic feature extraction to improve early risk stratification. Using a hybrid deep-learning architecture integrating convolutional spatial encoders and temporal attention mechanisms, the system automatically quantified structural, functional, and deformation-based cardiac parameters from standard echocardiographic videos. A dataset of labeled clinical outcomes enabled supervised model training, with performance evaluated across multiple metrics including accuracy, sensitivity, specificity, and predictive probability distribution. Results demonstrated that AI-derived strain patterns, wall-motion trajectories, and cycle-based temporal signatures significantly improved predictive performance compared to conventional echocardiographic assessments. Qualitative expert review additionally confirmed strong physiologic relevance in the model’s attention maps, supporting clinical interpretability. The findings suggest that AI-driven echocardiographic analytics can serve as a powerful prognostic tool, enhancing early detection of high-risk individuals and advancing the field toward precision cardiovascular care. Further large-scale validation is recommended to support integration into routine clinical workflows.