The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care. As artificial intelligence (AI) has entered the medical field in recent years, machine learning (ML) approaches have made progress in assisting healthcare professionals in optimizing personalized treatment in a given situation, in particular in electrocardiography and image interpretation. Artificial intelligence methodologies are increasingly being adopted into all aspects of patient care and are paving the way to minimally invasive or non-invasive treatment modalities. This article offers a state-of-the-art overview on milestones achieved, but also on future integration of this information into diagnostic and therapeutic measures, and its likely impact on all aspects of arrhythmia care.
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.
Galaxy Medical announced today the initiation of the SPACE-AF study with enrollment of the first two patients at Southlake Regional Health Centre in Newmarket, Canada. In the study, the CENTAURI Pulsed Electric Field (PEF) ablation system will be used to ablate both the pulmonary veins and posterior walls in patients with persistent atrial fibrillation (AF). Recently, Galaxy has also enrolled additional subjects in the ECLIPSE-AF study using the CARTO mapping system and CE-Marked catheters manufactured by Biosense Webster, achieving CENTAURI combatability with the three market leading cardiac mapping systems and their associated catheters. Atul Verma, MD, Head of the Heart Rhythm program at Southlake Regional Health Centre and Primary Investigator in the SPACE-AF study commented: "We are thrilled to launch the first study of focal ablation through a solid tip catheter with PEF to treat both the pulmonary veins and atrial targets beyond the pulmonary veins including the left atrial posterior wall. Patients with persistent atrial fibrillation often require electrical isolation beyond simple pulmonary vein isolation and for years we have been seeking a safe, predictable, and effective energy source to do so. In our first two cases using the CENTAURI focal PEF approach, I customized the lesion sets to each patient in our installed CARTO system. The procedures were nearly identical to our standard radiofrequency ablation cases, but with the added confidence of PEF as compared to thermal modalities. We look forward to reporting on the procedural safety and long-term efficacy results of this study."
It would be easy to wonder what Zachi Attia is doing in the cardiac operating rooms of one of America's most prestigious hospitals. He has no formal medical training or surgical expertise. The first time he watched a live procedure, he worried he might faint. But at Mayo Clinic, the 33-year-old machine learning engineer has become a central figure in one of the nation's most ambitious efforts to revamp heart disease treatment using artificial intelligence. Working side by side with physicians, he has built algorithms that in studies have shown a remarkable ability to unmask heart abnormalities long before patients begin experiencing symptoms.
Atrial fibrillation -- an irregular and often rapid heart rate -- is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. The study was published in Circulation. The investigators developed the artificial intelligence-based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.