Application of Deep Learning Techniques for Coronary Artery Disease Detection and Prediction: A Systematic Review

Coronary artery disease, a leading cause of death, occurs when coronary arteries narrow due to plaque or blood clots. It is possible to visualize these blood clots in the blood vessels through angiograms, a medical imaging technique. An angiogram is a type of medical imaging procedure used to see where blood clots or plaques have formed inside the coronary arteries. The conventional coronary angiogram or X-ray angiogram has been measured as a gold standard to predict coronary artery disease. Machine learning and deep learning techniques are widely deployed in many applications that include feature extraction, enhancement, and pattern analysis. The development of technology and the accessibility of vast amounts of data have made it relatively simple to analyze the condition of the coronary arteries, identify the disease, and make an early diagnosis. In this review paper, coronary artery disease assessment, angiograms, and various deep learning methods for coronary artery disease prediction are reviewed, and the assessment results are showcased.