Key result
Machine learning algorithms effectively identify AF from ECGs as a promising alternative to traditional single-point detection.
Why the study?
Prevalent AF detection relies on single-point ECGs that do not correlate symptoms with rhythm, whereas recent advances in machine learning and health monitors offer new opportunities for detection.
This review highlights the potential and challenges of using artificial intelligence and machine learning for the detection and management of atrial fibrillation via ECG and wearable devices.
Should not yet change AF screening practice; leaves open need for prospective outcome trials.
Atrial Fibrillation (AF) is the most common type of arrhythmia (Greek a-, loss + rhythmos, rhythm = loss of rhythm) leading to hospitalization in the United States. Though sometimes AF is asymptomatic, it increases the risk of stroke and heart failure in patients, in addition to lowering the health-related quality of life (HRQOL). AF-related care costs the healthcare system between $6.0 to $26 billion each year. Early detection of AF and clinical attention can help improve symptoms and HRQOL of the patient, as well as bring down the cost of care. However, the prevalent paradigm of AF detection depends on electrocardiogram (ECG) recorded at a single point in time and does not shed light on the relation of the symptoms with heart rhythm or AF. In the recent decade, due to the democratization of health monitors and the advent of high-performing computers, Machine Learning algorithms have been proven effective in identifying AF, from the ECG of patients. This paper provides an overview of the symptoms of AF, its diagnosis, and future prospects for research in the field.
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Bhattacharya et al. (2021) conducted a review in Atrial Fibrillation. Machine Learning algorithms was evaluated. Machine learning algorithms have proven effective in identifying atrial fibrillation from patient ECGs, offering a promising alternative to traditional single-point-in-time ECG detection.
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