How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Abstract

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

“…Gearhart et al used neural networks and cardiopulmonary function tests to compare the risk of cardiovascular-related mortality in patients who had heart failure with a left cardiopulmonary function test were followed up, and the results of the obtained tests and patient survival were composed into a sample set, which was randomly divided into a training set and a test set to compare cardiovascular-related mortality [ 14 ]. Olier et al applied a cardiac hemodynamic monitor to measure left atrial volume measurements and pulmonary vein parameters by the cardiac impedance method in patients with coronary artery disease at different times to observe the effects of coronary artery disease on patients' left atrial volume measurements and pulmonary veins to further guide clinical diagnosis [ 15 ]. Chaturvedi et al measured the hemodynamic indexes of heart failure patients by impedance hemogram detector and correlated them with BNP and left ventricular ejection fraction to explore their clinical significance in left atrial volume measurement and pulmonary vein assessment.…”

Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine‐learning algorithms

“…The application of AI and ML in cardiovascular disease has seen an exponential growth in recent years. 5 The primary aim of this paper is to develop different sets of ML-based models for improved prediction of incident or recurrent MI outcomes in US cohorts comprised of Commercial, Medicare and Medicaid health plans on the basis of administrative databases. This may help the scalability of prevention strategies and improved management in terms of healthcare cost savings and better quality of care.…”

“…Given the need to improve diagnosis, risk prediction and responsiveness to management, new approaches have been proposed, including using artificial intelligence (AI) and machine‐learning (ML) models. The application of AI and ML in cardiovascular disease has seen an exponential growth in recent years 5 …”

Comparing Outcomes in Asymptomatic and Symptomatic Atrial Fibrillation: A Systematic Review and Meta-Analysis of 81,462 Patients

“…Opportunistic screening by pulse palpation or ECG rhythm strip during a medical visit for any reason is recommended by the most recent guidelines and consensus documents in older patients (≥65 years old) [1,15]. Handheld devices with ECG capabilities may help in AF screening [59], but a role for wearables and 'smart' technology is emerging [39,60,61].…”