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Role of artificial intelligence in defibrillators: a narrative review

Grace Brown Orcid Logo, Samuel Conway, Mahmood Ahmad, Divine Adegbie, Nishil Patel, Vidushi Myneni, Mohammad Alradhawi, Niraj Kumar, Daniel Obaid Orcid Logo, Dominic Pimenta, Jonathan J H Bray

Open Heart, Volume: 9, Issue: 2, Start page: e001976

Swansea University Author: Daniel Obaid Orcid Logo

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DOI (Published version): 10.1136/openhrt-2022-001976

Abstract

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have...

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Published in: Open Heart
ISSN: 2053-3624
Published: BMJ 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa65389
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spelling v2 65389 2023-12-30 Role of artificial intelligence in defibrillators: a narrative review 1cb4b49224d4f3f2b546ed0f39e13ea8 0000-0002-3891-1403 Daniel Obaid Daniel Obaid true false 2023-12-30 BMS Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon. Journal Article Open Heart 9 2 e001976 BMJ 2053-3624 5 7 2022 2022-07-05 10.1136/openhrt-2022-001976 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University Not Required 2024-03-13T17:53:03.6353650 2023-12-30T14:28:35.1135636 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Grace Brown 0000-0001-7342-630x 1 Samuel Conway 2 Mahmood Ahmad 3 Divine Adegbie 4 Nishil Patel 5 Vidushi Myneni 6 Mohammad Alradhawi 7 Niraj Kumar 8 Daniel Obaid 0000-0002-3891-1403 9 Dominic Pimenta 10 Jonathan J H Bray 11 65389__29376__53ad430197f24aa180bbf6d1787be883.pdf 65389.pdf 2024-01-04T12:24:57.2576675 Output 674439 application/pdf Version of Record true This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license true eng http://creativecommons.org/licenses/by-nc/4.0/
title Role of artificial intelligence in defibrillators: a narrative review
spellingShingle Role of artificial intelligence in defibrillators: a narrative review
Daniel Obaid
title_short Role of artificial intelligence in defibrillators: a narrative review
title_full Role of artificial intelligence in defibrillators: a narrative review
title_fullStr Role of artificial intelligence in defibrillators: a narrative review
title_full_unstemmed Role of artificial intelligence in defibrillators: a narrative review
title_sort Role of artificial intelligence in defibrillators: a narrative review
author_id_str_mv 1cb4b49224d4f3f2b546ed0f39e13ea8
author_id_fullname_str_mv 1cb4b49224d4f3f2b546ed0f39e13ea8_***_Daniel Obaid
author Daniel Obaid
author2 Grace Brown
Samuel Conway
Mahmood Ahmad
Divine Adegbie
Nishil Patel
Vidushi Myneni
Mohammad Alradhawi
Niraj Kumar
Daniel Obaid
Dominic Pimenta
Jonathan J H Bray
format Journal article
container_title Open Heart
container_volume 9
container_issue 2
container_start_page e001976
publishDate 2022
institution Swansea University
issn 2053-3624
doi_str_mv 10.1136/openhrt-2022-001976
publisher BMJ
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
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 - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
active_str 0
description Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.
published_date 2022-07-05T17:52:59Z
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