Artificial Intelligence in Image-based Cardiovascular Disease Analysis

TL;DR

This paper reviews the current state and future potential of AI applications in image-based cardiovascular disease analysis, categorizing literature by anatomical structures and imaging modalities.

Contribution

It provides a structured review of AI in CVD image analysis, categorizing by anatomy and imaging techniques, and discusses challenges and future research directions.

Findings

AI has significantly advanced CVD image diagnostics.

Categorization by anatomy and modality aids understanding of AI applications.

Challenges include data limitations and model interpretability.

Abstract

Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into non-vessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like Computed tomography (CT) and Magnetic Resonance Imaging (MRI), which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.