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Respiratory Sounds

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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN.

Sensors (Basel, Switzerland)
Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing...

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
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...

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
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
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...

[Artificial intelligence in paediatric pneumology - opportunities and unanswered questions].

Klinische Padiatrie
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 ...

Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.

IEEE journal of biomedical and health informatics
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...

A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.

Scientific reports
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
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...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

Journal of medical Internet research
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...

Characteristics of lung sounds in early infants using automated analysis.

European journal of pediatrics
UNLABELLED: A new lung sound analysis software program has been developed. It can automatically select a typical lung sound spectrogram and calculate lung sound parameters using machine learning programs. This study aimed to clarify lung sound charac...