AIMC Topic: Heart Auscultation

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Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations.

Scientific reports May 31, 2025
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies wide...
Humans Neural Networks, Computer Female Sensitivity and Specificity Male Infant Deep Learning Adolescent Child, Preschool Child Heart Defects, Congenital Heart Auscultation Heart Murmurs Recurrent Neural Networks
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Segmentation-free Heart Pathology Detection Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a patient based on their heart sounds is a rather difficult skill to ma...
Humans Neural Networks, Computer Deep Learning Wavelet Analysis Heart Auscultation Heart Sounds
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[Artificial intelligence technology in cardiac auscultation screening for congenital heart disease: present and future].

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences Oct 25, 2020
The electronic stethoscope combined with artificial intelligence (AI) technology has realized the digital acquisition of heart sounds and intelligent identification of congenital heart disease, which provides objective basis for heart sound auscultat...
Humans Artificial Intelligence Algorithms Mass Screening Heart Defects, Congenital Heart Auscultation
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S1 and S2 Heart Sound Recognition Using Deep Neural Networks.

IEEE transactions on bio-medical engineering Feb 1, 2017
OBJECTIVE: This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S1-S2 and S2-S1 are not involved in the rec...
Humans Neural Networks, Computer Female Male Signal Processing, Computer-Assisted Stethoscopes Heart Auscultation Heart Sounds
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