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Respiratory Research
Published in: Respiratory Research 1/2019

Open Access 01-12-2019 | Cough | Research

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children

Authors: Paul Porter, Udantha Abeyratne, Vinayak Swarnkar, Jamie Tan, Ti-wan Ng, Joanna M. Brisbane, Deirdre Speldewinde, Jennifer Choveaux, Roneel Sharan, Keegan Kosasih, Phillip Della

Published in: Respiratory Research | Issue 1/2019

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Abstract

Background

The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser.

Methods

We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations.

Results

A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%). Conclusion: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders.

Trial registration

Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521​213: 11.09.2018.
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Metadata
Title
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children
Authors
Paul Porter
Udantha Abeyratne
Vinayak Swarnkar
Jamie Tan
Ti-wan Ng
Joanna M. Brisbane
Deirdre Speldewinde
Jennifer Choveaux
Roneel Sharan
Keegan Kosasih
Phillip Della
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Respiratory Research / Issue 1/2019
Electronic ISSN: 1465-993X
DOI
https://doi.org/10.1186/s12931-019-1046-6

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