Multimodality Imaging and Artificial Intelligence in Cardiovascular Disease: Advances, Integration, and Future Directions View PDF
Swarna Shree
Medicine, Meenakshi Medical College Hospital And Research Institute, Enathur, Tamil Nadu, India
*Ishrath Fathima
Medicine, Shadan Institute Of Medical Sciences, Hyderabad, Telangana, India
Challaboina Lakshmi Chandana
Medicine, Davao Medical School Foundation Inc, Davao, Philippines
Sadhvika Jeripeti
Medicine, Mamata Academy Of Medical Sciences, Hyderabad, Telangana, India
*Corresponding Author: Ishrath Fathima
Medicine, Shadan Institute Of Medical Sciences, Hyderabad, Telangana, India
Published on: 2025-04-30
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.
Keywords
Artificial intelligence, Cardiovascular disease, Diagnosis, Hybrid imaging, Multimodality imaging, Personalized medicine, Risk stratification
Introduction
Multimodality Imaging in CVDs
Multimodality imaging in CVDs is a rapidly evolving field that leverages various imaging techniques to enhance the diagnosis, management, and risk stratification of cardiovascular conditions [1, 2]. This approach is particularly beneficial in complex cases where single-modality imaging may not provide comprehensive insights. Multimodality imaging combines data from different imaging techniques, such as echocardiography, CT, MRI, and PET, to offer a more holistic view of cardiovascular health (Table 1). This integration is crucial for early detection, accurate diagnosis, and effective management of CVDs, especially in populations with specific needs, such as cancer survivors, the elderly, and women [3, 4].
Multimodality imaging plays a crucial role in the evaluation and management of various CVDs. The use of multiple imaging modalities allows for a comprehensive assessment of cardiac structure and function, aiding in the diagnosis, treatment, and monitoring of patients with heart conditions (Figure 1) [5]. In the context of congenital heart disease, the ACC/AHA/ASE/HRS/ISACHD/SCAI/SCCT/SCMR/ SOPE 2020 appropriate use criteria emphasizes the importance of multimodality imaging in the follow-up care of patients. This approach ensures a thorough evaluation of cardiac abnormalities and guides appropriate clinical decision-making [6]. The impact of multimodality imaging in the assessment of cardiovascular involvement in COVID-19 is highlighted in a study [7]. By utilizing various imaging techniques such as cardiac MRI and CT, researchers aim to identify cardiac pathophysiological mechanisms related to COVID-19 infections, providing valuable insights into the disease process [8, 9].
Furthermore, multimodality imaging has been instrumental in improving the definition and functional assessment of left ventricular non-compaction cardiomyopathy [10]. This approach allows for a more accurate characterization of cardiac abnormalities, leading to better management strategies for patients with this condition. In the realm of radiotherapy-induced cardiotoxicity, multimodality cardiovascular imaging plays a crucial role in screening for structural and functional abnormalities secondary to radiation therapy [11]. By utilizing echocardiography, cardiovascular CT, cardiac MRI, and nuclear cardiology, clinicians can effectively monitor and manage cardiac complications in cancer patients undergoing radiotherapy [12, 13]. Overall, the integration of multimodality imaging in the evaluation and management of CVDs has proven to be essential. From assessing large-vessel vasculitis to guiding thoracoscopic cardiac surgery and monitoring Anderson-Fabry disease, the use of multiple imaging modalities provides valuable clinical information for healthcare providers [14-16].
Multimodality imaging is vital for detecting ischemic and valvular heart disease in cancer patients, who are at increased risk due to shared risk factors and treatment-related cardiovascular toxicity. This approach aids in individual risk stratification and multidisciplinary decision-making, ensuring timely intervention and management [17]. In elderly patients, combining multimodal imaging with biomarker detection significantly improves diagnostic accuracy for coronary heart disease. Techniques like coronary CT angiography (CCTA) and echocardiography, when used alongside biomarkers, enhance sensitivity and specificity in diagnosis [18]. Multimodality imaging helps in assessing cardiovascular changes associated with aging, such as arterial wall thickening and myocardial fibrosis. Techniques like CT and ultrasound are used to measure coronary artery calcium and carotid intima-media thickness, aiding in advanced risk stratification and preventive strategy formulation [19]. Recent advancements in imaging modalities, including CCTA and MRI, have improved the detection and quantification of atherosclerotic plaques. These techniques help assess plaque stability and predict adverse cardiovascular events, facilitating personalized patient care [20].
Multimodality imaging is crucial for the early diagnosis and followup of radiation-induced heart disease [21, 22]. Techniques such as speckle-tracking echocardiography and cardiac magnetic resonance myocardial strain assessment provide valuable insights into subclinical disease and guide preventive measures [21]. Advanced imaging techniques allow for a detailed evaluation of cardiac chamber volumes, ventricular function, and tissue structure in cardiomyopathies. This comprehensive assessment aids in identifying specific etiologies and guiding therapeutic decisions [23].
This comprehensive approach enhances diagnostic accuracy, improves treatment outcomes, and ensures optimal care for patients with various cardiac conditions. While multimodality imaging offers numerous advantages, it is essential to consider the challenges and limitations associated with its implementation [24]. These include the high cost and limited accessibility of advanced imaging technologies, which may restrict their widespread use. Additionally, the need for specialized training and expertise to interpret multimodal data can be a barrier in some healthcare settings [25]. Despite these challenges, the potential of multimodality imaging to transform cardiovascular care remains significant, warranting continued research and development in this field.
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