ArticleBMC cardiovascular disorders2025
Detection of atrial fibrillation via artificial intelligence-assisted auscultation.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold.European journal of clinical investigation · 2026Review
- Evaluation of the DAMSUN-HF trial: the role of an artificial intelligence stethoscope in detecting reduced ejection fraction in patients living in a low-resource region.Heart failure reviews · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAtrial 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.
methodsA 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.
resultsThe 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.
conclusionThe 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.
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