AIMC Topic: Respiratory Sounds
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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Apr 18, 2025
BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of lung sounds is a key diagnostic tool but is prone to subjective variability. The integration of artif...
Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.
Sleep medicine
Mar 29, 2025
Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manual analysis to differentiate obstructive sleep apnea (OSA) from central sleep apnea (CSA). Existing portable devices, while valuable in detecting slee...
A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.
Scientific reports
Mar 15, 2025
Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine respiratory stability, and by detecting abnormal lung sounds via lung auscultation using a stethoscope t...
Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.
Journal of medical systems
Mar 4, 2025
Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecti...
AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations.
Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
Feb 18, 2025
BACKGROUND: AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for clinic visits.
An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.
Scientific reports
Feb 15, 2025
Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases an...
Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-Time Respiratory Sound Classification.
IEEE transactions on biomedical circuits and systems
Feb 11, 2025
This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ResNet on field programmable gate array (FPGA) for real-time monitoring. At the algorithmic level, m...
Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respiratory sound classification, in practice, it usually takes years to acquire a sufficiently sizeable unl...
[Artificial intelligence in paediatric pneumology - opportunities and unanswered questions].
Klinische Padiatrie
Feb 3, 2025
Artificial intelligence (AI) is already being used in most medical disciplines, including paediatric pneumology. This review describes current developments in AI-supported technologies and discusses their potential for the diagnosis and treatment of ...
Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.
European respiratory review : an official journal of the European Respiratory Society
Jan 8, 2025
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...
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