Key result
Deep learning AI improves AF detection accuracy over traditional algorithms across various ECG modalities.
Why the study?
Early detection of atrial fibrillation is hindered by variability in ECG signals, and recent AI deep learning techniques have addressed some detection barriers.
Do deep learning AI models improve the detection accuracy of atrial fibrillation from ECG signals compared to traditional algorithms?
Systematic Review (n=12)
Do deep learning AI models improve the detection accuracy of atrial fibrillation from ECG signals compared to traditional algorithms?
Deep learning models, particularly attention-based and hierarchical networks, improve the accuracy of atrial fibrillation detection across various ECG modalities.
Strengthens case for deep learning AI in ECG-based AF detection; extends validation to attention-based models across modalities.
Atrial fibrillation is one of the most prevalent types of cardiac arrhythmias and has strong connections to stroke and heart failure. Its early detection is often hindered by variability in EGC signals, especially in cases of paroxysmal AF. The development of AI has recently removed some stubborn barriers to AF detection through the application of deep learning techniques. This paper systematically reviews 12 current studies published between 2023 and 2024 that employed AI models for AF detection with varying ECG modalities, including single-lead, 12-lead, and image-based ECG. The paper is organized by the data types present in the studies, the preprocessing of these data, the AI architectures used, and validation protocols in use within the clinical setup. Our findings indicate that attention-based networks, hierarchical models, and features extraction techniques, such as chirplet transforms, have effectively improved the detection accuracy of AF. In addition, we look at the role of wearable and mobile technologies regarding ECG by comparing smart device-based AI algorithms with traditional ones used in diagnostic practice. Also discussed will be challenges such as dataset imbalance, signal noise from wearable devices, interpretable AI models, and cross-platform validation. The paper summarizes with future directions of AI in improving diagnostic inclusion, toward transparency, and embedding these technologies into everyday clinical practice.
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Imane et al. (2025) conducted a systematic review in Atrial fibrillation (n=12). Deep learning AI models vs. Traditional diagnostic algorithms was evaluated on Detection accuracy of atrial fibrillation. Deep learning AI models, including attention-based networks and hierarchical models, effectively improved the detection accuracy of atrial fibrillation across various ECG modalities.
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