Publication Name	World Journal of Cardiology
Manuscript ID	67939
Country	United States

Received	2021-05-08 01:48
Peer-Review Started	2021-05-08 01:51
To Make the First Decision
Return for Revision	2021-06-29 00:08
Revised	2021-07-10 15:01
Second Decision	2021-08-13 02:57
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-08-13 08:29
Articles in Press	2021-08-13 08:29
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-10-13 11:31
Publish the Manuscript Online	2021-10-21 08:56

ISSN	1949-8462 (online)
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/
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Category	Cardiac & Cardiovascular Systems
Manuscript Type	Minireviews
Article Title	Artificial intelligence and machine learning in cardiovascular computed tomography
Manuscript Source	Invited Manuscript
All Author List	Karthik Seetharam, Premila Bhat, Maxine Orris, Hejmadi Prabhu, Jilan Shah, Deepak Asti, Preety Chawla and Tanveer Mir
Funding Agency and Grant Number
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
Key Words	Computed tomography; Machine learning; Artificial intelligence; Cardiovascular 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.
Publish Date	2021-10-21 08:56
Citation	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
URL	https://www.wjgnet.com/1949-8462/full/v13/i10/546.htm
DOI	https://dx.doi.org/10.4330/wjc.v13.i10.546

Full Article (PDF)	WJC-13-546.pdf
Full Article (Word)	WJC-13-546.docx
Manuscript File	67939_Auto_Edited-JPY.docx
Answering Reviewers	67939-Answering reviewers.pdf
Audio Core Tip	67939-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	67939-Conflict-of-interest statement.pdf
Copyright License Agreement	67939-Copyright license agreement.pdf
Peer-review Report	67939-Peer-review(s).pdf
Scientific Misconduct Check	67939-Scientific misconduct check.pdf
Scientific Editor Work List	67939-Scientific editor work list.pdf