Machine learning in the detection and management of atrial fibrillation

Abstract

Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Typical data flow in machine learning applications for atrial fibrillation detection.

Keywords: Arrhythmia; Artificial intelligence; Deep learning; Electrophysiology; Neural network.

Fig. 1

Panels A and B: Illustration of a smartphone-based ECG device (A) with an automated rhythm classification based on traditional machine-learning algorithms (B). Panel C: schematic depiction of a simple neural network designed with one hidden layer. The width of connecting arrows signifies differently weighted connections between layers