Spectral analysis of bruits with an electronic stethoscope enhances screening of carotid stenosis and plaques beyond conventional auscultation.

Background: Stroke is the second leading cause of death worldwide, with carotid stenosis being a primary contributor. Therefore, stroke prevention would benefit from accessible carotid stenosis screening tools. Historically, acoustic stethoscopes were used to listen to the carotid artery, but this method is now outdated due to its subjectivity and inconsistent sensitivity and specificity in detecting stenosis. In contrast, electronic stethoscopes record audio, enabling precise and objective analysis. To overcome traditional auscultation limitations, our study introduces a signal analysis scheme to evaluate the electronic stethoscope as a potential screening tool for carotid plaques and severe stenosis. Methods: We included 94 patients undergoing duplex ultrasound (DUS) for recent transient ischemic attack (TIA) or pre-operative assessment for carotid endarterectomy. DUS served as the clinical reference for determining plaque presence and estimating carotid stenosis. Participants held their breath during electronic stethoscope measurements at two points along each carotid artery: (I) proximal, on the common carotid; and (II) distal, near the bifurcation. From these recordings, we extracted 10 spectral features and utilized multivariable binary logistic regression for predicting plaques and severe stenosis, applying 10-fold cross-validation for internal validation. We constructed the receiver operating characteristic (ROC) curve by plotting the true positive rate against the false positive rate at various cutoff settings. We reported the area under the curve (AUC), along with sensitivity and specificity, which were determined using a single optimal cutoff point. Results: For detecting >70% stenosis using distal location recordings, the analysis yielded training and testing AUCs of 0.87 and 0.79, sensitivity of 84.9% and 78.6%, and specificity of 73.6% and 72.1%, respectively. Using proximal location recordings, training and testing AUCs were 0.84 and 0.73, with sensitivities of 79.8% and 60.7%, and specificities of 76.0% and 75.6%, respectively. For detecting the presence of plaques, proximal location measurements showed training and testing AUCs of 0.79 and 0.7, sensitivities of 54.9% and 51.9%, and specificities of 91.9% and 78.8%, respectively. Conclusions: Our findings demonstrate that the electronic stethoscope with spectral analysis is promising for identifying severe stenosis but has limited sensitivity for detecting any plaque. The performance obtained with this approach is superior to that attainable with conventional auscultation. This approach could serve as a promising, user-friendly screening tool, particularly in resource-limited settings.