Multimodality Imaging and Artificial Intelligence in Cardiovascular Disease: Advances, Integration, and Future Directions

Abstract

A comprehensive review is essential to understand the evolving role of multimodality imaging and artificial intelligence (AI) in cardiovascular diseases (CVDs) diagnosis and management. This review highlights the integration of various imaging techniques and AI-driven advancements to enhance diagnostic accuracy, risk stratification, and personalized treatment approaches. By addressing current innovations, challenges, and future directions, this review provides a foundation for optimizing cardiovascular imaging and improving patient outcomes. This review explores the significance of multimodality imaging in CVDs, emphasizing its role in combining echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for a more comprehensive assessment. The integration of AI in cardiovascular imaging is examined, particularly in automating image analysis, enhancing diagnostic precision, and facilitating risk prediction models. The review further discusses hybrid imaging techniques (HIT) and their ability to merge anatomical and functional data, improving disease detection and management. The application of multimodality imaging in personalized medicine, with a focus on patient-specific diagnostics and treatment strategies, is also addressed. Additionally, challenges such as accessibility, cost, and AI integration into clinical workflows are analyzed. The review concludes by outlining future research directions aimed at refining imaging technologies and AI applications for better cardiovascular care.