Recent Advances in Coronary Artery Disease Detection, Risk Assessment: Integrating with Artificial Intelligence : Review Article

There is a growing global occurrence of diabetes mellitus with dyslipidemia along with hypertension, and other cardiometabolic disorders, Coronary artery disease (CAD) ruins the major cause of disease and death in current society despite inventions in imaging technology, pharmacological medicine, and interventional cardiology. Recently, advancements in Artificial Intelligence (AI) technology, such as Machine Learning (ML) and Deep Learning (DL) algorithms, have noticeably enhanced CAD diagnostic accuracy, risk assessment, and preventive decisions. The present article aims to comprehensively summarise an AI-driven system that influences two corresponding sources of information: Electrocardiography (ECG) images and patient biometric data, including age, gender, Blood Pressure (BP), and heart rate. Combining these methods provides a more complete and accurate prediction model for CAD detection. The study focuses on AI-powered ECG analysis algorithms as well as machine learning algorithms for CAD predictive models. Methods used in AI, including algorithmic paradigms implemented with algorithms and validation techniques, will be discusse in details in this paper. This article explains the AI advances in diagnosing CAD