Key result
The TAP-CRNN model accurately recognized systolic murmurs in patients with ventricular septal defects, achieving a sensitivity of 96.0% and a specificity of 96.7% on the test dataset.
Why the study?
Accurately identifying abnormal heart sound patterns depends largely on clinical experience and is challenging for inexperienced clinicians.
Does a temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model accurately recognize systolic murmurs in patients with ventricular septal defects compared to standard CNN or CRNN models?
Observational (n=76)
No
Does a temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model accurately recognize systolic murmurs in patients with ventricular septal defects compared to standard CNN or CRNN models?
A novel TAP-CRNN deep learning model can accurately identify systolic murmurs in patients with ventricular septal defects, offering a potential automated screening tool for structural heart disease.
Answers are grounded in this paper and cite their sources.
Supports AI-assisted VSD murmur screening; leaves open prospective validation before clinical adoption.
Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSDs and 25 subjects without a significant heart malformation were obtained in this study. Subsequently, the soundtracks were divided into different training and testing sets to establish the recognition system and evaluate the performance. The automatic murmur recognition system was based on a novel temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model. On analyzing the performance using the test data that comprised 178 VSD heart sounds and 60 normal heart sounds, a sensitivity rate of 96.0% was obtained along with a specificity of 96.7%. When analyzing the heart sounds recorded in the second aortic and tricuspid areas, both the sensitivity and specificity were 100%. We demonstrated that the proposed TAP-CRNN system can accurately recognize the systolic murmurs of VSD patients, showing promising potential for the development of software for classifying the heart murmurs of several other structural heart diseases.
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Wang et al. (2020) conducted an observational in Ventricular septal defect (VSD) (n=76). Temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) algorithm vs. Echocardiography (gold standard) was evaluated on Sensitivity for recognizing systolic murmurs. The TAP-CRNN model accurately recognized systolic murmurs in patients with ventricular septal defects, achieving a sensitivity of 96.0% and a specificity of 96.7% on the test dataset.
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