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Machine learning applications in cardiac computed tomography: a composite systematic review

Jonathan Bray, Moghees Ahmad Hanif, Mohammad Alradhawi, Jacob Ibbetson, Surinder Singh Dosanjh, Sabrina Lucy Smith, Mahmood Ahmad Orcid Logo, Dominic Pimenta Orcid Logo

European Heart Journal Open, Volume: 2, Issue: 2

Swansea University Author: Jonathan Bray

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DOI (Published version): 10.1093/ehjopen/oeac018

Abstract

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase,...

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Published in: European Heart Journal Open
ISSN: 2752-4191
Published: Oxford University Press (OUP) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62367
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spelling 2023-01-19T09:06:19.6882660 v2 62367 2023-01-18 Machine learning applications in cardiac computed tomography: a composite systematic review c8b2c8d8ea027cdd8b0a318ab9d89f78 Jonathan Bray Jonathan Bray true false 2023-01-18 FGMHL Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT. Journal Article European Heart Journal Open 2 2 Oxford University Press (OUP) 2752-4191 Machine learning; Artificial intelligence; Cardiac computed tomography 17 3 2022 2022-03-17 10.1093/ehjopen/oeac018 COLLEGE NANME Medicine, Health and Life Science - Faculty COLLEGE CODE FGMHL Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-01-19T09:06:19.6882660 2023-01-18T17:44:31.2357592 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Jonathan Bray 1 Moghees Ahmad Hanif 2 Mohammad Alradhawi 3 Jacob Ibbetson 4 Surinder Singh Dosanjh 5 Sabrina Lucy Smith 6 Mahmood Ahmad 0000-0001-9107-3704 7 Dominic Pimenta 0000-0002-3179-7249 8 62367__26336__977b5130b0ec4220ae3548e64e94f00f.pdf 62367.pdf 2023-01-19T09:01:46.1562789 Output 739077 application/pdf Version of Record true © The Author(s) 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License true eng https://creativecommons.org/licenses/by-nc/4.0/
title Machine learning applications in cardiac computed tomography: a composite systematic review
spellingShingle Machine learning applications in cardiac computed tomography: a composite systematic review
Jonathan Bray
title_short Machine learning applications in cardiac computed tomography: a composite systematic review
title_full Machine learning applications in cardiac computed tomography: a composite systematic review
title_fullStr Machine learning applications in cardiac computed tomography: a composite systematic review
title_full_unstemmed Machine learning applications in cardiac computed tomography: a composite systematic review
title_sort Machine learning applications in cardiac computed tomography: a composite systematic review
author_id_str_mv c8b2c8d8ea027cdd8b0a318ab9d89f78
author_id_fullname_str_mv c8b2c8d8ea027cdd8b0a318ab9d89f78_***_Jonathan Bray
author Jonathan Bray
author2 Jonathan Bray
Moghees Ahmad Hanif
Mohammad Alradhawi
Jacob Ibbetson
Surinder Singh Dosanjh
Sabrina Lucy Smith
Mahmood Ahmad
Dominic Pimenta
format Journal article
container_title European Heart Journal Open
container_volume 2
container_issue 2
publishDate 2022
institution Swansea University
issn 2752-4191
doi_str_mv 10.1093/ehjopen/oeac018
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
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description Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.
published_date 2022-03-17T04:21:55Z
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