ArticleScientific reports2023
A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.European journal of pediatrics · 2026Observational
- From Fixed-Frequency to Tunable: Advances in Acoustic Sensors for Physiological Acoustic Monitoring.Sensors (Basel, Switzerland) · 2026Review
- Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.PLOS digital health · 2026Article
- Characteristics of lung sounds in early infants using automated analysis.European journal of pediatrics · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence.NPJ primary care respiratory medicine · 2024Article
- The analysis of lung sounds in infants and children with a history of wheezing/asthma using an automatic procedure.BMC pulmonary medicine · 2024Article
- Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers.Sensors (Basel, Switzerland) · 2024Article
- Influence of Gaussian White Noise on Medical Students' Capacity to Accurately Identify Pulmonary Sounds.Noise & healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation, a cost-effective and non-invasive part of physical examination, is essential to diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission, storage, and analysis of lung sounds. We aimed to develop a machine learning model to classify pediatric respiratory sounds. Lung sounds were digitally recorded during routine physical examinations at a pediatric pulmonology outpatient clinic from July to November 2019 and labeled as normal, crackles, or wheezing. Ensemble support vector machine models were trained and evaluated for four classification tasks (normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing) using K-fold cross-validation (K = 10). Model performance on a prospective validation set (June to July 2021) was compared with those of pediatricians and non-pediatricians. Total 680 clips were used for training and internal validation. The model accuracies during internal validation for normal vs. abnormal, crackles vs. wheezing, normal vs. crackles, and normal vs. wheezing were 83.68%, 83.67%, 80.94%, and 90.42%, respectively. The prospective validation (n = 90) accuracies were 82.22%, 67.74%, 67.80%, and 81.36%, respectively, which were comparable to pediatrician and non-pediatrician performance. An automated classification model of pediatric lung sounds is feasible and maybe utilized as a screening tool for respiratory disorders in this pandemic era.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.