Key result
Machine learning algorithms demonstrated high diagnostic accuracy for detecting atrial fibrillation from electrocardiograms, with a pooled sensitivity of 97% and specificity of 97%.
Why the study?
Atrial fibrillation is common and can cause adverse cardiovascular outcomes but is often hard to detect, prompting the need to systematically evaluate the diagnostic accuracy of machine learning algorithms for AF detection in ECG signals.
Do machine learning algorithms accurately detect atrial fibrillation from ECGs?
Meta-Analysis
Do machine learning algorithms accurately detect atrial fibrillation from ECGs?
Effect estimate: Sensitivity 97% (95% CI 0.94-0.99)
Machine learning algorithms, particularly deep learning models, demonstrate high diagnostic accuracy for detecting atrial fibrillation from ECGs, supporting their potential integration into wearable devices.
Supports integration of deep learning ECG analysis into wearables and screening; reinforces diagnostic performance and guides prospective validation studies.
Background: Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals. Methods: The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity. Results: A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94-0.99) and 97% (95% CI: 0.95-0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets. Conclusions: ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.
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Xie et al. (2024) conducted a meta-analysis in Atrial fibrillation. Machine learning algorithms was evaluated on Pooled sensitivity for detecting atrial fibrillation (Sensitivity 97%, 95% CI 0.94-0.99). Machine learning algorithms demonstrated high diagnostic accuracy for detecting atrial fibrillation from electrocardiograms, with a pooled sensitivity of 97% and specificity of 97%.
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