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Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope i...

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Autores principales: Ben Ali, Walid, Pesaranghader, Ahmad, Avram, Robert, Overtchouk, Pavel, Perrin, Nils, Laffite, Stéphane, Cartier, Raymond, Ibrahim, Reda, Modine, Thomas, Hussin, Julie G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692711/
https://www.ncbi.nlm.nih.gov/pubmed/34957230
http://dx.doi.org/10.3389/fcvm.2021.711401
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author Ben Ali, Walid
Pesaranghader, Ahmad
Avram, Robert
Overtchouk, Pavel
Perrin, Nils
Laffite, Stéphane
Cartier, Raymond
Ibrahim, Reda
Modine, Thomas
Hussin, Julie G.
author_facet Ben Ali, Walid
Pesaranghader, Ahmad
Avram, Robert
Overtchouk, Pavel
Perrin, Nils
Laffite, Stéphane
Cartier, Raymond
Ibrahim, Reda
Modine, Thomas
Hussin, Julie G.
author_sort Ben Ali, Walid
collection PubMed
description Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.
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spelling pubmed-86927112021-12-23 Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble Ben Ali, Walid Pesaranghader, Ahmad Avram, Robert Overtchouk, Pavel Perrin, Nils Laffite, Stéphane Cartier, Raymond Ibrahim, Reda Modine, Thomas Hussin, Julie G. Front Cardiovasc Med Cardiovascular Medicine Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed. Frontiers Media S.A. 2021-12-08 /pmc/articles/PMC8692711/ /pubmed/34957230 http://dx.doi.org/10.3389/fcvm.2021.711401 Text en Copyright © 2021 Ben Ali, Pesaranghader, Avram, Overtchouk, Perrin, Laffite, Cartier, Ibrahim, Modine and Hussin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Ben Ali, Walid
Pesaranghader, Ahmad
Avram, Robert
Overtchouk, Pavel
Perrin, Nils
Laffite, Stéphane
Cartier, Raymond
Ibrahim, Reda
Modine, Thomas
Hussin, Julie G.
Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title_full Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title_fullStr Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title_full_unstemmed Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title_short Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble
title_sort implementing machine learning in interventional cardiology: the benefits are worth the trouble
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692711/
https://www.ncbi.nlm.nih.gov/pubmed/34957230
http://dx.doi.org/10.3389/fcvm.2021.711401
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