The Role of Multimodality Imaging in HIV-Associated Cardiomyopathy

Gambahaya, Ellise T.; Rana, Rimsha; Bagchi, Shashwatee; Sharma, Garima; Sarkar, Sudipa; Goerlich, Erin; Cupido, Blanche; Mukherjee, Monica; Hays, Allison G.

doi:10.3389/fcvm.2021.811593

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…There have been several recent survey articles on multi-modality for cardiovascular diseases with different focuses (5)(6)(7)(8)(9). In this survey, we focus on providing a near comprehensive review of multi-modal imaging for cardiovascular diseases since 2018.…”

Section: Introductionmentioning

confidence: 99%

Applications of AI in multi-modal imaging for cardiovascular disease

Milosevic,

Jin,

Singh

et al. 2024

Front. Radiol.

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Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.

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