Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT)

Michelle C. Williams, Jonathan R. Weir-McCall, Lauren A. Baldassarre, Carlo N. De cecco, Andrew D. Choi, Damini Dey, Marc R. Dweck, Ivana Isgum, Márton Kolossvary, Jonathon Leipsic, Andrew Lin, Michael T. Lu, Manish Motwani, Koen Nieman, Leslee Shaw, Marly van Assen, Edward Nicol

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial intelligence (AI), and machine learning (ML) in particular, are rapidly transforming the world around us. In healthcare, AI/ML has the potential to improve every point in the clinical pathway. For cardiovascular computed tomography (CT) AI/ML could aid patient selection and screening, referrals and scheduling, image acquisition and reconstruction, image analysis and diagnosis, report generation, prognostication and risk stratification, management recommendations and follow-up. However, there are important challenges that must be considered for the development, assessment, and implementation of AI/ML to ensure that it is safe, reliable, cost effective, and improves outcomes for patients. In this white paper from the Society of Cardiovascular Computed Tomography we discuss current state-of-the-art applications of AI/ML in cardiovascular CT, highlight challenges and considerations for both research and implementation in clinical practice, and explore the future of this technology. This white paper reflects the expert opinions of the writing group and SCCT guidelines were followed for the selection of the writing group and development of the white paper.
Original languageEnglish
Pages (from-to)519-532
JournalJournal of Cardiovascular Computed Tomography
Volume18
Issue number6
DOIs
Publication statusPublished - 30 Aug 2024

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