TY - JOUR
T1 - Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT)
AU - Williams, Michelle C.
AU - Weir-McCall, Jonathan R.
AU - Baldassarre, Lauren A.
AU - De cecco, Carlo N.
AU - Choi, Andrew D.
AU - Dey, Damini
AU - Dweck, Marc R.
AU - Isgum, Ivana
AU - Kolossvary, Márton
AU - Leipsic, Jonathon
AU - Lin, Andrew
AU - Lu, Michael T.
AU - Motwani, Manish
AU - Nieman, Koen
AU - Shaw, Leslee
AU - van Assen, Marly
AU - Nicol, Edward
PY - 2024/8/30
Y1 - 2024/8/30
N2 - 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.
AB - 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.
U2 - 10.1016/j.jcct.2024.08.003
DO - 10.1016/j.jcct.2024.08.003
M3 - Article
SN - 1934-5925
VL - 18
SP - 519
EP - 532
JO - Journal of Cardiovascular Computed Tomography
JF - Journal of Cardiovascular Computed Tomography
IS - 6
ER -