Saeed Amal ^1* Douglas Sawyer ² Arda Könik ³

• ¹ Bioengineering Dept., College of Engineering, Northeastern University, The Roux Institute, Northeastern University, Portland, United States
• ² Maine Medical Center, Maine Health, Portland, Maine, United States
• ³ Dana–Farber Cancer Institute, Boston, Massachusetts, United States

Today's digital health aims to provide an improved efficiency of healthcare delivery, and personalized, and timely disease care. Cardiovascular Disease (CVD) is a leading cause of death worldwide. In the United States, 1 out of 3 adults have some form of CVD. It is projected that nearly half of the US population will have at least one type of CVD by 2035, with total direct and indirect costs potentially surpassing $1 trillion (1-3). Medical imaging data encompass multiple modalities that are primarily utilized in silos. These include Computed Tomography (CT), Magnetic Resonance Imaging (MRI), CT-derived fractional flow reserve (CT-FFR), cardiac MRI, whole-heart dynamic 3D cardiac MRI perfusion, 3D cardiac MRI late gadolinium enhancement, cardiac positron emission tomography (PET), echocardiography and coronary angiography. However, only a few modalities are utilized in hybrid configurations, such as Positron Emission Tomography combined with Computed Tomography (PET/CT),Single-Photon Emission Computed Tomography combined with CT (SPECT/CT), Echocardiography and invasive angiography. Integrating these different imaging modalities becomes a burden on clinicians as it can lead to added complexity, potential inaccuracies, and increased healthcare costs. This research topic focused on fusion techniques that enable the integration and modeling of these multiple modalities to offer complementary information that will help improve CVD care. These modalities will leverage Machine Learning (ML) and Deep Learning (DL) techniques as well as other state-of-the-art techniques. Following are some insights and findings from this research topic: Milosevic et al.) conducted a systematic and comprehensive review on the state-of-the-art multi-modal medical data fusion in the context of CVD (4). Their review indicated that there are limited open multimodal datasets that are constrained both in size and modality scope. This scarcity of open datasets of labeled pathologies contributes to the comparatively few published papers on the diagnosis or prediction of cardiovascular diseases and conditions. The review indicated that over the last 5 years, there has been a considerable amount of work in artificial intelligence employing fusion techniques of multi-modal imaging involving various magnetic resonance imaging (MRI) and CT scans. However, the integration of modalities like x-ray, echocardiography, and non-imaging modalities remains relatively scarce.