Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst

Disclosures

Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Artificial Intelligence and Cardiac Devices

Most of the pacemaker and defibrillator functions use rule-based algorithms. Rate response feature in a pacemaker, which incorporates the ability of the device to vary the pacing rate based on an input from a biosensor; tachycardia detection and deliverance of an appropriate therapy by an implantable defibrillator, are some of the illustrations of the rule-based decision-making. A rule-based algorithm is referred to as a simplest form of AI but it differs from ML in its inability to learn.[33] In rule-based algorithms, rules are laid down by humans based on the domain expertise, whereas ML methodologies learn actively from the training data and create their own rules for decision-making, which may not be transparent in some instances (Supplementary material online, Section S2 and Table S1).

Machine learning algorithms are finding their use with cardiac devices both in arrhythmia detection and prediction of future events. Machine learning methods have been employed in automated external defibrillators in the development of shock advice algorithms.[7,34–36] Recently, Nguyen et al.[37] developed an algorithm for the detection of shockable and non-shockable rhythms using ML. They used both boosting classifier and convolutional neural network as a feature extractor with a sensitivity and specificity of 95.21% and 99.31%, respectively. This shock advice algorithm was a considerable improvement over existing algorithms and in keeping with the standards set by the American Heart Association guidelines.[38] More recently, a DL technique was introduced to identify any cardiac device model from a chest radiograph.[39]

Machine learning methods have been used to improve cardiac resynchronization therapy (CRT) outcomes prediction, paving the way for better patient selection.[40,41] In a proof of concept, single centre study, ML algorithm in conjunction with natural language processing was applied to electronic health records.[42] This model successfully identified subgroups of patients who were unlikely to benefit from CRT. An ML algorithm using naive Bayes classifier using patient variables including age, sex, QRS duration and morphology, left ventricular ejection fraction and end-diastolic diameter, New York Heart Association functional class, presence of AF, and epicardial left ventricular lead was superior to existing guidelines in predicting event-free survival post-CRT.[43]

Machine learning algorithms including random forests and convoluted neural networks, when applied to the AF signature burden obtained using continuous remote monitoring data in patients with cardiac implantable electronic devices, were superior in predicting stroke compared to the widely used CHA2DS2-VASc score. An ensemble method using ML model in conjunction with CHA2DS2-VASc score had better sensitivity and specificity when compared to using CHA2DS2-VASc score alone and improved AUC from 0.52 to 0.63.[44]

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