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

Is Artificial Intelligence Bridging the Gaps in Arrhythmia Care?

Recent research into AI-enabled ECG has rekindled interest into observational-based research. Artificial intelligence-enabled ECG has been shown to identify patients with persistent AF, left ventricular systolic dysfunction, and HCM and the list is likely to grow in time with emerging evidence from ongoing research. This ubiquitous cardiac investigation has a potential to be a powerful screening tool at point of care, an innovation that may have a significant impact on community-based diagnosis of latent cardiac conditions, even more so in the under privileged parts of the world.

In an acute setting, AI-enabled ECG may aid in the rapid identification of life threatening electrolyte imbalance and patients at the imminent risk of cardiac arrest who may need more intensive monitoring. Severe restrictions imposed by the recent COVID-19 pandemic resulted in some of the AI-enabled technologies such as QT interval monitoring using AI-enabled mobile devices, approved by regulatory authorities to see the light of the day in clinical practice.[96]

Advancements in sensor technology and wireless communications with ability to link devices over internet of things have made continuous heart rhythm monitoring feasible, albeit resulting in an exponential increase in data to be analysed. Review of such data by a skilled personnel is nearly impossible due to time and resource constraints. Artificial intelligence solutions can effectively analyse these data in a time and a resource efficient manner. Artificial intelligence-assisted near real-time analysis of data from wearable devices has prompted the contemplation of newer research into novel treatment strategies such as pill in the pocket anticoagulation following an episode of AF.[97]

Machine learning algorithms have shown their utility to further personalized patient care by improving existing guidelines, which aid in clinical decision-making regarding anticoagulation in the at-risk population and patient selection for cardiac device therapy. Superiority of ML methodologies over traditional rule-based algorithms in handling big data may facilitate data analysis from multiple data sources to identify the impending risk of life threatening arrhythmia or heart failure episodes in a timely manner.

Artificial intelligence-enabled technological advancements are aiding in arrhythmia focus identification prior to EP procedures. In time, AI-enabled ECG may better contribute to accurate localization of accessory pathway or arrhythmia focus. There is a vast potential for the application of ML methodologies to intracardiac data including EAM, for better characterization of an arrhythmia to aid in selection of the ideal ablation strategy.

Limitations and Challenges

Machine learning methodologies are not error free, best example being overfitting, a phenomenon resulting from a disproportionate number of features in comparison to the amount of data in the training set.[98] As a result of overfitting, the ML algorithm performs very well on the training set whilst performing poorly on the test set with poor generalizability. It has to be appreciated that traditional statistical methods may be superior in analysing lesser amount of data whilst ML offers the ability to handle high computational power to classify large amounts of data efficiently.

Some of the ML methodologies are opaque, and as a consequence, it may not possible to verify how an algorithm arrives at its conclusions. This current lack of transparency in methodology, often referred to as black box nature of ML,[99] can affect clinicians' confidence when applying ML-based technologies in active clinical decision-making. Defining regulatory guidelines for these self-learning, non-transparent yet accurate ML methods can be challenging.

Artificial intelligence is a data science, and hence, the importance of the quality of the data used to train and validate ML algorithms cannot be overstated.[100] There is a greater need for collaboration and data sharing between research centres to collate large quantities of robust healthy data to improve the generalizability of ML methodologies. This highlights yet another challenge relating to data security and privacy. More transparency about how data are shared and stricter adherence to data management laws is essential in research involving ML methodologies.

Increasing use of ML algorithms in clinical decision-making[101] is likely to challenge the concept of personal responsibility and a physicians fiduciary relationship towards patients,[102] necessitating regulatory guidelines to clarify the distribution of liability in the event of mishaps involving AI-aided technologies. Cardiologists in the near term are likely to be keen on AI-aided rather than AI-dictated clinical decision-making for patient management.

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