EVOLUTION OF IMAGING MODALITIES IN THE DIAGNOSIS OF CORONARY ARTERY DISEASE NARRATIVE REVIEW

Abstract

Background: Coronary artery disease (CAD) remains the leading cause of global mortality, prompting the continuous evolution of diagnostic imaging to enhance early detection, risk stratification, and clinical decision-making. The transition from conventional invasive angiography to advanced non-invasive modalities—such as coronary computed tomography angiography (CCTA) and cardiac magnetic resonance imaging (CMR)—has significantly altered diagnostic paradigms. The growing role of artificial intelligence (AI) in augmenting imaging accuracy and workflow efficiency has added further momentum to this shift.

Objective: This narrative review explores the evolution of imaging technologies in diagnosing CAD, emphasizing the integration of AI in CT and MRI-based modalities and their implications for clinical practice.

Main Discussion Points: The review highlights advances in anatomical and functional imaging, such as FFR-CT, dynamic CT perfusion, AI-assisted plaque analysis, and myocardial tissue characterization with CMR. It evaluates the diagnostic performance, clinical utility, and emerging multimodal approaches. The review also addresses limitations in current literature, including methodological variability, limited generalizability, and underrepresentation of long-term outcomes.

Conclusion: While non-invasive and AI-enhanced imaging techniques are transforming CAD diagnosis, evidence supporting their clinical impact remains moderate. Future research should focus on large-scale, prospective studies to validate these innovations and guide standardized integration into practice.