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World J Cardiol. Oct 26, 2021; 13(10): 546-555
Published online Oct 26, 2021. doi: 10.4330/wjc.v13.i10.546
Artificial intelligence and machine learning in cardiovascular computed tomography
Karthik Seetharam, Premila Bhat, Maxine Orris, Hejmadi Prabhu, Jilan Shah, Deepak Asti, Preety Chawla, Tanveer Mir
Karthik Seetharam, Department of Cardiology, West Virgina University, Morgan Town, NY 26501, United States
Premila Bhat, Maxine Orris, Jilan Shah, Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
Hejmadi Prabhu, Deepak Asti, Preety Chawla, Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
Tanveer Mir, Department of Internal Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
Author contributions: Seetharam K and Bhat P contributed equally to this work; Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, and Mir T designed the research study; Seetharam K, Bhat P, Asti D, and Mir T performed the research; Seetharam K and Bhat P wrote the manuscript; all authors have read and approve the final manuscript.
Conflict-of-interest statement: The authors declare that there is no any conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Karthik Seetharam, MD, Academic Research, Department of Cardiology, West Virgina University, Heart and Vascular Institute West Virginia University 1 Medical Center Drive, Morgan Town, NY 26501, United States. skarthik87@yahoo.com
Received: May 8, 2021
Peer-review started: May 8, 2021
First decision: June 29, 2021
Revised: July 10, 2021
Accepted: August 13, 2021
Article in press: August 13, 2021
Published online: October 26, 2021
Processing time: 166 Days and 7.1 Hours
Core Tip

Core Tip: Machine learning (ML), a subset of artificial intelligence, contains multiple algorithms which include supervised, unsupervised, reinforcement and deep learning. These algorithms can greatly augment multiple aspects in computed tomography which include automated segmentation, diagnosis, and stratification based on risk. Outputs need to be carefully assessed by the medical team for any potential biases. For the future of computed tomography and cardiovascular imaging, ML algorithms need to be integrated in clinical care.