Key result
Integrating artificial intelligence into multimodality imaging workflows holds transformative potential for enhancing diagnostic accuracy and efficiency in the management of ischemic heart disease.
Why the study?
Prompt and precise diagnosis of IHD is essential to avert complications, prompting an evaluation of the current state and complementary use of noninvasive multimodality imaging and AI integration.
AI-enhanced multimodality imaging holds transformative potential for improving diagnostic precision and guiding therapeutic decisions in ischemic heart disease.
AI may improve ischemic heart disease diagnosis via multimodality imaging; extends evidence synthesis across modalities and future AI applications.
Ischemic heart disease (IHD) is a major global health issue, frequently resulting in myocardial infarction and ischemic cardiomyopathy. Prompt and precise diagnosis is essential to avert complications such as heart failure and sudden cardiac death. Although invasive coronary angiography remains the gold standard for high-risk patients, noninvasive multimodality imaging is becoming more prevalent for those at low-to-intermediate risk. This review evaluated the current state of multimodality imaging in IHD, including echocardiography, nuclear cardiology, cardiac magnetic resonance imaging (MRI), computed tomography (CT) angiography, and invasive coronary angiography. Each modality has distinct strengths and limitations, and their complementary use provides a comprehensive assessment of cardiac health. Integrating artificial intelligence (AI) into imaging workflows holds promise for enhancing diagnostic accuracy and efficiency. AI algorithms can optimize image acquisition, processing, and interpretation of complex imaging data. Emerging technologies like 4D flow MRI, molecular imaging, and hybrid systems [e.g., positron emission tomography (PET)/MRI, PET/CT] integrate anatomical, functional, and molecular data, providing comprehensive insights into cardiac pathology and potentially revolutionizing the management of IHD. This review also explored the clinical applications and impact of multimodality imaging on patient outcomes, emphasizing its role in improving diagnostic precision and guiding therapeutic decisions. Future directions include AI-driven decision support systems and personalized medicine approaches. Addressing regulatory and ethical challenges, such as data privacy and algorithm transparency, is crucial for the broader adoption of these advanced technologies. This review highlighted the transformative potential of AI-enhanced multimodality imaging in improving the diagnosis and management of IHD.
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Sekar et al. (2024) conducted a review in Ischemic heart disease (IHD). Artificial intelligence in multimodality imaging was evaluated. Integrating artificial intelligence into multimodality imaging workflows holds transformative potential for enhancing diagnostic accuracy and efficiency in the management of ischemic heart disease.
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