The AI-based electronic stethoscope accurately detected atrial fibrillation from phonocardiogram signals, achieving an AUC of 0.928 compared to standard 12-lead ECG.
Observational (n=496)
No
Does an AI-based electronic stethoscope accurately detect atrial fibrillation compared to standard 12-lead ECG in patients with suspected or confirmed AF?
An AI-based electronic stethoscope demonstrated high diagnostic accuracy for detecting atrial fibrillation, offering a promising and accessible tool for primary healthcare screening.
Effect estimate: AUC 0.928 (95% CI 0.885-0.970)
p-value: p=<0.001
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.
Guo et al. (Fri,) conducted a observational in Atrial fibrillation (n=496). AI-based electronic stethoscope (FINZ-PCG) vs. Standard 12-lead ECG was evaluated on Area under the ROC curve (AUC) for discriminating atrial fibrillation in the validation dataset (AUC 0.928, 95% CI 0.885-0.970, p=<0.001). The AI-based electronic stethoscope accurately detected atrial fibrillation from phonocardiogram signals, achieving an AUC of 0.928 compared to standard 12-lead ECG.