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Background <p>Atrial fibrillation (AF) is a prevalent arrhythmia with significant health risks, often underdiagnosed due to limitations in traditional screening methods. This study investigates the effectiveness of an AI-based electronic stethoscope for AF screening, comparing it to other portable devices.</p> Methods <p>A retrospective study was conducted using 496 cardiac sound recordings from patients with and without AF. The recordings were divided into derivation and validation datasets. An AI model, combining ResNet34 and a 12-layer Vision Transformer (ViT), was developed and trained on the derivation dataset. The model’s performance was evaluated using sensitivity, specificity, accuracy, positive and negative predictive values, and the area under the receiver operating characteristic (ROC) curve (AUC). Additionally, a non-consecutive day twice cardiac sound collection was performed on 74 samples to assess the model’s consistency.</p> Results <p>The AI model achieved high performance metrics in both derivation and validation datasets. In the derivation dataset, sensitivity was 0.95 (95% CI, 0.90–0.97), specificity was 0.90 (95% CI, 0.83–0.94), accuracy was 0.92 (95% CI, 0.90–0.96), positive predictive value was 0.92 (95% CI, 0.87–0.96), and negative predictive value was 0.93 (95% CI, 0.86–0.96). In the validation dataset, sensitivity was 0.94 (95% CI, 0.88–0.98), specificity was 0.91 (95% CI, 0.83–0.96), accuracy was 0.93 (95% CI, 0.89–0.96), positive predictive value was 0.93 (95% CI, 0.86–0.97), and negative predictive value was 0.93 (95% CI, 0.85–0.97). The AUC for the derivation dataset was 0.92 (95% CI, 0.89–0.96), and for the validation dataset, it was 0.93 (95% CI, 0.88–0.97). The non-consecutive day cardiac sound collection resulted in a Cohen’s Kappa value of 0.74, indicating good consistency in the model’s judgments.</p> Conclusion <p>The AI-based electronic stethoscope shows promise as a reliable and accessible tool for AF screening, with potential applications in primary healthcare and general population screening.</p>

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Detection of atrial fibrillation via artificial intelligence-assisted auscultation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Yongzhe Guo,
  • Huizhong Lin,
  • Hui Chen,
  • Xianhong Wu,
  • Yukun Luo

摘要

Background

Atrial fibrillation (AF) is a prevalent arrhythmia with significant health risks, often underdiagnosed due to limitations in traditional screening methods. This study investigates the effectiveness of an AI-based electronic stethoscope for AF screening, comparing it to other portable devices.

Methods

A retrospective study was conducted using 496 cardiac sound recordings from patients with and without AF. The recordings were divided into derivation and validation datasets. An AI model, combining ResNet34 and a 12-layer Vision Transformer (ViT), was developed and trained on the derivation dataset. The model’s performance was evaluated using sensitivity, specificity, accuracy, positive and negative predictive values, and the area under the receiver operating characteristic (ROC) curve (AUC). Additionally, a non-consecutive day twice cardiac sound collection was performed on 74 samples to assess the model’s consistency.

Results

The AI model achieved high performance metrics in both derivation and validation datasets. In the derivation dataset, sensitivity was 0.95 (95% CI, 0.90–0.97), specificity was 0.90 (95% CI, 0.83–0.94), accuracy was 0.92 (95% CI, 0.90–0.96), positive predictive value was 0.92 (95% CI, 0.87–0.96), and negative predictive value was 0.93 (95% CI, 0.86–0.96). In the validation dataset, sensitivity was 0.94 (95% CI, 0.88–0.98), specificity was 0.91 (95% CI, 0.83–0.96), accuracy was 0.93 (95% CI, 0.89–0.96), positive predictive value was 0.93 (95% CI, 0.86–0.97), and negative predictive value was 0.93 (95% CI, 0.85–0.97). The AUC for the derivation dataset was 0.92 (95% CI, 0.89–0.96), and for the validation dataset, it was 0.93 (95% CI, 0.88–0.97). The non-consecutive day cardiac sound collection resulted in a Cohen’s Kappa value of 0.74, indicating good consistency in the model’s judgments.

Conclusion

The AI-based electronic stethoscope shows promise as a reliable and accessible tool for AF screening, with potential applications in primary healthcare and general population screening.