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Review
. 2023 Sep;16(9):1209-1223.
doi: 10.1016/j.jcmg.2023.05.012. Epub 2023 Jul 19.

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care

Damini Dey  1 , Rima Arnaout  2 , Sameer Antani  3 , Aldo Badano  4 , Louis Jacques  5 , Huiqing Li  6 , Tim Leiner  7 , Edward Margerrison  4 , Ravi Samala  4 , Partho P Sengupta  8 , Sanjiv J Shah  9 , Piotr Slomka  1 , Michelle C Williams  10 , W Patricia Bandettini  6 , Vandana Sachdev  6
Affiliations
Review

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care

Damini Dey et al. JACC Cardiovasc Imaging. 2023 Sep.

Abstract

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.

Keywords: AI algorithms; artificial intelligence; cardiovascular imaging; data science; deep learning; machine learning.

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Conflict of interest statement

Funding Support and Author Disclosures The content of this manuscript is solely the responsibility of the authors and does not necessarily reflect the official views of the National Heart, Lung, and Blood Institute, National Institutes of Health, or the United States Department of Health and Human Services. The National Heart, Lung, and Blood Institute (NHLBI) Workshop on Artificial Intelligence in Cardiovascular Imaging: Translating Science to Patient Care, held on June 27 and 28, 2022, was supported by the Division of Cardiovascular Sciences, NHLBI. Dr Antani has been supported by the Intramural Research Program of the National Library of Medicine and National Institutes of Health (NIH). Dr Arnaout has been supported by the NIH, the Department of Defense, and the Gordon and Betty Moore Foundation. Dr Dey has received software royalties from Cedars Sinai; and funding support from NIH/NHLBI grants (1R01HL148787-01A1 and 1R01HL151266). Dr Leiner has served on the Advisory Board for Cart-Tech B.V. and AI4Med; has been a clinical advisor for Quantib B.V.; has been a consultant for Guerbet; and has received funding support from the Netherlands Heart Foundation. Dr Sengupta has served on the Advisory Boards of Echo IQ and RCE Technologies; and has received funding support from NSF Award: 2125872 and NRT-HDR: Bridges in Digital Health. Dr Shah has received consulting fees from AstraZeneca, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol Myers Squib, Cytokinetics, Edwards Lifesciences, Eidos, Gordian, Intellia, Ionis, Merck, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sardocor, Shifamed, Tenax, Tenaya, and United Therapeutics; and has received funding support from the NIH (U54 HL160273, R01 HL140731, R01 HL149423), Corvia, and Pfizer. Dr Slomka has received software royalties from Cedars Sinai; and has received funding support from the NIH/NHLBI grant 1R35HL161195-01. Dr Williams has been a speaker at lectures sponsored by Canon Medical Systems and the Siemens Healthineers; and has received funding support from the British Heart Foundation (FS/ICRF/20/26002). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

Figure 1:
Figure 1:. NHLBI grants supporting AI in cardiovascular imaging over the past ten years.
The NIH internal data platform was used to search for grants from fiscal year 2013 to 2022 using free-text search in the title and abstract. The terms used were: (“machine learning” and “imaging”) AND (“deep learning” and “imaging”) AND (“AI” and “imaging”). Fifty-three grants were finalized after removing non-human studies and lung and blood related studies, as well as removing non-target grants by manual curation. The grant information is accessible using the publicly available NIH Research Portfolio Online Reporting Tools (NIH RePORT) system. Early-stage investigator grants, clinical trials, and U-grants have all become more common in recent years. Grants span imaging modalities, but typically focus on only one modality per grant.
Figure 2:
Figure 2:. AI can be used to address use cases across the entire cardiovascular imaging workflow.
Where AI is typically thought of with respect to downstream applications (e.g., image analysis and prognosis), it is also proving useful upstream, from patient selection and protocoling for imaging studies, to image acquisition at the clinical point of care, signal processing, image denoising, attenuation correction, and reconstruction. These use cases are applicable across cardiovascular imaging modalities. At each step in the clinical workflow, AI can help provide speed and standardization and improve image quality. Finally, improved imaging can impact patient care as it is used to predict outcomes, improve prognostication, and connect to other clinical data modalities (e.g. text reports, lab data).
Figure 3:
Figure 3:. The computing infrastructure landscape for AI is complex, including several types of stakeholders and several computing resources and tools.
While a crowded playing field has its advantages, the complexity could also make it more difficult for medical centers to participate in AI research and to collaborate with each other. Stakeholders include medical centers (dark blue), large cloud providers (orange), as well as intermediate platforms (light blue) that offer certain computing features and tools but ultimately rely on large cloud providers for core services. Given the complexity of the computing landscape, the computing option(s) chosen by an institution affect availability of certain tools and/or datasets and can affect ease of collaboration with other institutions. AWS, Amazon Web Services. GCP, Google Cloud Platform.
Figure 4:
Figure 4:. Overview of different components of AI/ML-enabled software as a medical device in premarket submissions.
The intended use of the device (light blue) influences the data used for device development and the associated reference standard (orange). The device development process (blue) includes the algorithm development, a resulting locked model, and model evaluation. Clinical performance evaluation (red) consists of several interrelated components such as study design, bias and generalization assessment, selection of appropriate endpoints and metrics and selection of testing data that is representative of the target population.
Central Illustration:
Central Illustration:. Translating AI into patient care for cardiovascular imaging across modalities requires several components and has potential for improving care.
Data management, algorithm innovation, technical infrastructure, regulatory policies, and human capital must all continue to be developed in concert in order to enable clinical validation and testing, to ultimately achieve clinical impact for patient care.
See this image and copyright information in PMC

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