Vol. 6, Issue 1, Part A (2024)

Cardiovascular diseases (CVDs) remain one of the leading causes of morbidity and mortality worldwide, necessitating advancements in diagnostic technologies to improve patient outcomes. Traditional imaging modalities, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), have been instrumental in visualizing cardiac structures and detecting pathological changes. However, these techniques often rely on expert interpretation, which can be subject to human error and inconsistencies. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising tool to enhance the diagnostic accuracy of cardiovascular imaging by automating image analysis and improving the interpretation of complex datasets. AI algorithms can analyze vast amounts of imaging data, identifying subtle patterns that may not be visible to the human eye, thereby enabling early diagnosis and personalized treatment planning. This integration has the potential to revolutionize cardiovascular care by facilitating earlier detection of diseases such as coronary artery disease, myocardial infarction, and heart failure. Furthermore, the combination of AI with MRI and CT imaging could enhance clinical decision-making, improve patient outcomes, and reduce healthcare costs by enabling more efficient and accurate diagnoses. This paper reviews the current state of AI integration with cardiovascular imaging technologies, specifically MRI and CT, and explores their future potential in early diagnosis, predictive modeling, and personalized treatment in cardiology.