Ai-based Atherosclerosis Detection Model Using Cardiovascular Imaging Data

Abstract

Atherosclerosis Is A Chronic Cardiovascular Condition Characterized By The Gradual Accumulation Of Plaque Within Arterial Walls, Resulting In Restricted Blood Flow And Increased Risk Of Heart Attacks And Strokes. Early Detection Is Essential For Effective Clinical Intervention; However, Traditional Diagnostic Techniques Such As Coronary Angiography, CT Scans, MRI, And Ultrasound Rely Heavily On Manual Interpretation. These Methods Are Labor-intensive, Prone To Inter-observer Variability, And Often Detect The Disease At Advanced Stages. This Project Proposes An AI-based Atherosclerosis Detection Framework Leveraging Machine Learning (ML) And Deep Learning (DL) Techniques To Automatically Analyze Cardiovascular Imaging Data, Identify Plaque Regions, Classify Disease Severity, And Support Clinical Decision-making. The System Integrates Image Preprocessing, Feature Extraction, And Convolutional Neural Network (CNN)-based Analysis To Detect Early-stage Plaques With High Accuracy. Real-time Implementation Allows Continuous Monitoring And Early Alerts, Reducing Cardiovascular Risk. Testing On Annotated Datasets Demonstrates Improved Diagnostic Performance, Including Higher Sensitivity, Precision, And Reduced False Positives, Compared To Conventional Methods.

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