Emerging technologies and applications in multimodality imaging for ischemic heart disease

What is it about?

This paper provides a comprehensive review of recent advances in multimodality imaging technologies for the diagnosis and management of ischemic heart disease (IHD), focusing on the integration of artificial intelligence (AI) and machine learning. It explores how AI enhances image acquisition, processing, interpretation, and risk stratification across various imaging modalities, including echocardiography, CT, MRI, and nuclear imaging. The study evaluates the current applications, strengths, and limitations of these technologies, and discusses challenges related to data standardization, validation, and clinical translation. Finally, it outlines future directions for AI-driven multimodal imaging to support precision cardiology and improve patient outcomes in IHD.

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Photo by Ali Hajiluyi on Unsplash

Photo by Ali Hajiluyi on Unsplash

Why is it important?

Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, and timely, accurate diagnosis is crucial for effective management. By reviewing the integration of emerging imaging technologies and artificial intelligence, this paper highlights how these innovations can enhance diagnostic accuracy, efficiency, and personalized risk assessment in IHD. This paper is important because it provides critical insights into the current capabilities and future potential of AI-enabled multimodality imaging, offering a roadmap for advancing precision cardiology and improving patient care.

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