Abstract
In recent years, numerous applications for artificial intelligence (AI) in cardiology
have been found, due in part to large digitized data sets and the evolution of high-performance
computing. In the discipline of cardiac electrophysiology (EP), a number of clinical,
imaging, and electrical waveform data are considered in the diagnosis, prognostication,
and management of arrhythmias, which lend themselves well to automation through AI.
But equally relevant, AI offers a unique opportunity to discover novel EP concepts
and improve clinical care through its inherent, hierarchical tenets of self-learning.
In this review we focus on the application of AI in clinical EP and summarize state-of-the
art, large, clinical studies in the following key domains: (1) electrocardiogram-based
arrhythmia and disease classification; (2) atrial fibrillation source detection; (3)
substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias;
and (4) predicting outcomes after cardiac resynchronization therapy. Many are small,
single-centre, proof-of-concept investigations, but they still show ground-breaking
performance of deep learning, a subdomain of AI, which surpasses traditional statistical
analysis. Larger studies, for instance classifying arrhythmias from electrocardiogram
recordings, have further provided external validation of their high accuracy. Ultimately,
the performance of AI is dependent on the quality of the input data and the rigour
of algorithm development. The field is still nascent and several barriers will need
to be overcome, including prospective validation in large, well labelled data sets
and more seamless information technology-based data collection/integration, before
AI can be adopted into broader clinical EP practice. This review concludes with a
discussion of these challenges and future work.
Résumé
Ces dernières années, de nombreuses applications relatives à l'intelligence artificielle
(IA) en cardiologie ont été découvertes, en partie grâce aux vastes ensembles de données
numérisées et à l'évolution du calcul haute performance. Dans la discipline de l'électrophysiologie
cardiaque (EP), un certain nombre de données cliniques, d'imagerie et de types d'ondes
électriques sont prises en compte dans le diagnostic, le pronostic et la prise en
charge des arythmies, qui se prêtent bien à l'automatisation par l'IA. Mais de façon
toute aussi pertinente, l'IA offre une occasion unique de découvrir de nouveaux concepts
associés à l'EP et d'améliorer les soins cliniques grâce à ses principes inhérents
et hiérarchiques d'auto-apprentissage. Dans cet article de revue, nous nous concentrons
sur l'application de l'IA à l'EP clinique et synthétisons l'état de l'art, les grandes
études cliniques dans les domaines clés suivants : (1) classification des arythmies
et des pathologies basée sur l'électrocardiogramme; (2) détection de la source de
la fibrillation auriculaire; (3) évaluation du substrat et du risque de fibrillation
auriculaire et de tachyarythmie ventriculaire; et (4) prédiction du pronostic après
une thérapie de resynchronisation cardiaque. Il s'agit souvent de petites études,
monocentriques, de validation du concept, mais elles n'en présentent pas moins les
performances révolutionnaires de l'apprentissage profond, un sous-domaine de l'IA,
qui surpasse l'analyse statistique traditionnelle. Des études de plus grande envergure,
portant par exemple sur la classification des arythmies à partir d'enregistrements
d'électrocardiogrammes, ont fourni une validation externe de leur grande précision.
En définitive, les performances de l'IA dépendent de la qualité des données d'entrée
et de la rigueur du développement des algorithmes. Ce domaine en est encore à ses
débuts et plusieurs obstacles devront être surmontés, notamment la validation prospective
dans de grands ensembles de données bien identifiées et une collecte/intégration des
données plus transparente basée sur les technologies de l'information, avant que l'IA
ne puisse être adoptée plus largement dans la pratique clinique de l'EP. Cette revue
se termine par une discussion sur ces défis et les développements futurs.
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Article info
Publication history
Published online: July 28, 2021
Accepted:
July 25,
2021
Received:
May 30,
2021
Footnotes
See page 256 for disclosure information.
Identification
Copyright
© 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
