Key result
Subclinical AF is linked to higher mortality and stroke risk, while AI-ECG aids early detection.
Advances in AI-enabled ECG and wearable devices may improve the early detection of subclinical atrial fibrillation, potentially reducing its associated stroke and mortality burden.
Supports AI-ECG evaluation for subclinical AF detection; leaves open outcome benefits in prospective trials.
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.
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Kashou et al. (2021) conducted a review in Subclinical Atrial Fibrillation. Artificial intelligence-enabled ECG (AI-ECG) was evaluated. Subclinical atrial fibrillation is associated with increased mortality and stroke risk, and AI-enabled ECG algorithms may help identify high-risk patients for earlier detection and treatment.
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