ArticleBMC pediatrics2014
Validation of computerized wheeze detection in young infants during the first months of life.
Article in BMC pediatrics, 2014. 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, 19 citations in OpenAlex.
- Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.European journal of pediatrics · 2026Observational
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Artificial intelligence and wheezing in children: where are we now?Frontiers in medicine · 2024Review
- Effects of Chest Physiotherapy in Preterm Infants with Respiratory Distress Syndrome: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- 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
- Article
- Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices.Biomedical engineering online · 2018Article
- Passive Nocturnal Physiologic Monitoring Enables Early Detection of Exacerbations in Children with Asthma. A Proof-of-Concept Study.American journal of respiratory and critical care medicine · 2018Observational
- Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study.BMJ open respiratory research · 2017Article
- Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians' classification of lung sounds from video recordings.BMJ open respiratory research · 2016Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral respiratory diseases are associated with specific respiratory sounds. In contrast to auscultation, computerized lung sound analysis is objective and can be performed continuously over an extended period. Moreover, audio recordings can be stored. Computerized lung sounds have rarely been assessed in neonates during the first year of life. This study was designed to determine and validate optimal cut-off values for computerized wheeze detection, based on the assessment by trained clinicians of stored records of lung sounds, in infants aged <1 year.
methodsLung sounds in 120 sleeping infants, of median (interquartile range) postmenstrual age of 51 (44.5-67.5) weeks, were recorded on 144 test occasions by an automatic wheeze detection device (PulmoTrack®). The records were retrospectively evaluated by three trained clinicians blinded to the results. Optimal cut-off values for the automatically determined relative durations of inspiratory and expiratory wheezing were determined by receiver operating curve analysis, and sensitivity and specificity were calculated.
resultsThe optimal cut-off values for the automatically detected durations of inspiratory and expiratory wheezing were 2% and 3%, respectively. These cutoffs had a sensitivity and specificity of 85.7% and 80.7%, respectively, for inspiratory wheezing and 84.6% and 82.5%, respectively, for expiratory wheezing. Inter-observer reliability among the experts was moderate, with a Fleiss' Kappa (95% confidence interval) of 0.59 (0.57-0.62) for inspiratory and 0.54 (0.52 - 0.57) for expiratory wheezing.
conclusionComputerized wheeze detection is feasible during the first year of life. This method is more objective and can be more readily standardized than subjective auscultation, providing quantitative and noninvasive information about the extent of wheezing.
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