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Towards Artificial Intelligence-Based Decision Support for Large-Scale Screening for Atrial Fibrillation.

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    Summary
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    Deep neural networks show promise for early atrial fibrillation (AF) detection using simplified ECG screening. This technology achieved higher accuracy than automated analysis and can be integrated into wearable devices for continuous monitoring.

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    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Medical Technology

    Background:

    • Atrial fibrillation (AF) is a common arrhythmia linked to increased stroke, heart failure, and mortality risks.
    • Early detection of AF, particularly in asymptomatic or paroxysmal stages, is crucial for timely intervention.
    • Current screening methods may not be sufficient for widespread, early detection in at-risk populations.

    Purpose of the Study:

    • To evaluate the efficacy of deep neural networks (DNNs) for atrial fibrillation detection using simplified electrocardiogram (ECG) screening.
    • To assess the performance of DNNs compared to automated ECG analysis in a large-scale population study.
    • To explore the potential of explainable AI (XAI) for diagnostic support and the integration of DNNs into wearable devices for continuous AF monitoring.

    Main Methods:

    • A handheld ECG device (MyDiagnostick) acquired data from 7295 individuals aged 65+ in a pharmacy-based trial.
    • A validated deep neural network model was applied to 12-lead ECG data for feature extraction and AF detection.
    • Explainable AI techniques were used to interpret model decisions and assess feasibility for wearable technology.

    Main Results:

    • Deep neural networks achieved an F1-score of 86%, outperforming automated ECG stick analysis (81%) for atrial fibrillation detection.
    • The study demonstrated the potential for integrating DNNs into wearable devices by reducing model weights by 99% with minimal accuracy loss.
    • Explainable AI successfully highlighted ECG segments indicative of atrial fibrillation, aiding diagnostic interpretation.

    Conclusions:

    • Deep neural networks offer a highly effective tool for enhancing population-wide atrial fibrillation screening and early diagnosis.
    • The integration of DNNs into wearable devices is feasible, paving the way for continuous AF monitoring.
    • Explainable AI enhances the clinical utility of AI models by providing interpretable insights for medical professionals.