Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13(10): 546-555 [PMID: 34754399 DOI: 10.4330/wjc.v13.i10.546]
Corresponding Author of This Article
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
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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
Abstract
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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.
