Skip to main content

Main menu

  • Home
  • Current issue
  • ERJ Early View
  • Past issues
  • ERS Guidelines
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • Open access
    • COVID-19 submission information
    • Peer reviewer login
  • Alerts
  • Subscriptions
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

User menu

  • Log in
  • Subscribe
  • Contact Us
  • My Cart

Search

  • Advanced search
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

Login

European Respiratory Society

Advanced Search

  • Home
  • Current issue
  • ERJ Early View
  • Past issues
  • ERS Guidelines
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • Open access
    • COVID-19 submission information
    • Peer reviewer login
  • Alerts
  • Subscriptions

Late Breaking Abstract - Accuracy of artificial intelligence in detecting pathological breath sounds in children using digital stethoscopes

Ajay Kevat, Anaath Kalirajah, Robert Roseby
European Respiratory Journal 2020 56: 4798; DOI: 10.1183/13993003.congress-2020.4798
Ajay Kevat
Monash Children's Hospital, Melbourne, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ajaykevat@gmail.com
Anaath Kalirajah
Monash Children's Hospital, Melbourne, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert Roseby
Monash Children's Hospital, Melbourne, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
Loading

Abstract

Background: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes (DS) with artificial intelligence (AI) could improve reliable detection of these sounds.

Objective: We aimed to independently test the abilities of AI developed for the purpose of detecting wheezes/rhonchi and crackles in children.

Methods: 192 auscultation recordings collected from children using two different DS (Clinicloud™ and Littman™) were each tagged as containing wheezes/rhonchi, crackles or neither by a paediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis. Untagged versions of the recordings were submitted for analysis by a blinded AI algorithm (StethoMe™ AI) trained to detect pathologic paediatric breath sounds, which generated a probability score of the likelihood of presence of crackles or wheeze/rhonchi. AI outcome was compared with tagged outcomes on a per-recording basis, with receiver operating characteristic curves used to identify optimal cutoffs representing best AI performance.

Results: With optimised AI thresholds, crackle detection positive percent agreement (PPA) was 0.95 and negative percent agreement (NPA) was 0.99 for Clinicloud recordings; for Littman-collected sounds PPA was 0.82 and NPA was 0.96. Wheeze detection PPA and NPA were 0.90 and 0.97 respectively (Clinicloud auscultation), with PPA 0.80 and NPA 0.95 for Littman recordings.

Conclusions: AI can detect crackles and wheeze from breath sounds obtained using different DS devices with a degree of accuracy that approaches (or exceeds) that of clinicians. Careful integration into clinical practice may improve standards of care.

  • Diagnosis
  • Experimental approaches
  • Wheezing

Footnotes

Cite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4798.

This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2020
Previous
Back to top
Vol 56 Issue suppl 64 Table of Contents
  • Table of Contents
  • Index by author
Email

Thank you for your interest in spreading the word on European Respiratory Society .

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Late Breaking Abstract - Accuracy of artificial intelligence in detecting pathological breath sounds in children using digital stethoscopes
(Your Name) has sent you a message from European Respiratory Society
(Your Name) thought you would like to see the European Respiratory Society web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Late Breaking Abstract - Accuracy of artificial intelligence in detecting pathological breath sounds in children using digital stethoscopes
Ajay Kevat, Anaath Kalirajah, Robert Roseby
European Respiratory Journal Sep 2020, 56 (suppl 64) 4798; DOI: 10.1183/13993003.congress-2020.4798

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Late Breaking Abstract - Accuracy of artificial intelligence in detecting pathological breath sounds in children using digital stethoscopes
Ajay Kevat, Anaath Kalirajah, Robert Roseby
European Respiratory Journal Sep 2020, 56 (suppl 64) 4798; DOI: 10.1183/13993003.congress-2020.4798
Reddit logo Technorati logo Twitter logo Connotea logo Facebook logo Mendeley logo

Jump To

  • Article
  • Info & Metrics
  • Tweet Widget
  • Facebook Like
  • Google Plus One

More in this TOC Section

  • Late Breaking Abstract - RECEIVER trial: sustained use and improved outcomes with digitally supported COPD co-management
  • Late Breaking Abstract - Impact of increasing notification thresholds for remote respiratory monitoring in patients with chronic lung disease
  • The COVID-19 risk perception and concern: a survey of patients on long term oxygen therapy or domiciliary noninvasive ventilation
Show more m-Health/e-health

Related Articles

Navigate

  • Home
  • Current issue
  • Archive

About the ERJ

  • Journal information
  • Editorial board
  • Press
  • Permissions and reprints
  • Advertising

The European Respiratory Society

  • Society home
  • myERS
  • Privacy policy
  • Accessibility

ERS publications

  • European Respiratory Journal
  • ERJ Open Research
  • European Respiratory Review
  • Breathe
  • ERS books online
  • ERS Bookshop

Help

  • Feedback

For authors

  • Instructions for authors
  • Publication ethics and malpractice
  • Submit a manuscript

For readers

  • Alerts
  • Subjects
  • Podcasts
  • RSS

Subscriptions

  • Accessing the ERS publications

Contact us

European Respiratory Society
442 Glossop Road
Sheffield S10 2PX
United Kingdom
Tel: +44 114 2672860
Email: journals@ersnet.org

ISSN

Print ISSN:  0903-1936
Online ISSN: 1399-3003

Copyright © 2023 by the European Respiratory Society