ArticlePloS one2020
A wheeze recognition algorithm for practical implementation in children.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Objective detection of wheeze at home by parents through a digital device: usage patterns and relationship with SABA administration.Pediatric pulmonology · 2025Trial
- Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.European journal of pediatrics · 2026Observational
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- Current Challenges in Pediatric Asthma.Children (Basel, Switzerland) · 2024Review
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.Biosensors · 2024Article
- Clinical efficacy and satisfaction of a digital wheeze detector in a multicentre randomised controlled trial: the WheezeScan study.ERJ open research · 2024Article
- The impact of a digital wheeze detector on parental disease management of pre-school children suffering from wheezing-a pilot study.Pilot and feasibility studies · 2021Article
- Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study.JMIR pediatrics and parenting · 2021Article
- Wheezing Characteristics and Predicting Reactivity to Inhaled β2-Agonist in Children for Home Medical Care.Frontiers in pediatrics · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children.
methodsA wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity.
resultsThe detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively.
conclusionsThe wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.
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