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DC Field | Value | Language |
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dc.contributor.author | Kalirajah A. | en |
dc.contributor.author | Roseby R. | en |
dc.contributor.author | Kevat A. | en |
dc.date.accessioned | 2021-05-14T09:45:04Z | en |
dc.date.available | 2021-05-14T09:45:04Z | en |
dc.date.copyright | 2020 | en |
dc.date.created | 20201209 | en |
dc.date.issued | 2020-12-09 | en |
dc.identifier.citation | Respiratory Research. 21 (1) (no pagination), 2020. Article Number: 253. Date of Publication: 29 Sep 2020. | en |
dc.identifier.issn | 1465-9921 | en |
dc.identifier.uri | https://repository.monashhealth.org/monashhealthjspui/handle/1/28926 | en |
dc.description.abstract | Background: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose. Method(s): One hundred and ninety two auscultation recordings collected from children using two different digital stethoscopes (ClinicloudTM and LittmanTM) were each tagged as containing wheezes, crackles or neither by a pediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis, with a subset validated by a blinded second clinician. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds. Result(s): With optimized AI detection 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. Conclusion(s): AI can detect crackles and wheeze with a reasonably high degree of accuracy from breath sounds obtained from different digital stethoscope devices, although some device-dependent differences do exist.Copyright © 2020 The Author(s). | en |
dc.language | en | en |
dc.language | English | en |
dc.publisher | BioMed Central Ltd | en |
dc.relation.ispartof | Respiratory Research | en |
dc.title | Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. | en |
dc.type | Article | en |
dc.identifier.doi | http://monash.idm.oclc.org/login?url=http://dx.doi.org/10.1186/s12931-020-01523-9 | - |
dc.publisher.place | United Kingdom | en |
dc.identifier.pubmedid | 32993620 [http://www.ncbi.nlm.nih.gov/pubmed/?term=32993620] | en |
dc.identifier.source | 632995884 | en |
dc.identifier.institution | (Kevat, Kalirajah, Roseby) Department of Paediatrics, Monash University, Melbourne, Australia (Kevat, Roseby) Department of Respiratory Medicine, Monash Children's Hospital, 246 Clayton Road, Clayton, Melbourne, VIC 3168, Australia | en |
dc.description.address | A. Kevat, Department of Paediatrics, Monash University, Melbourne, Australia. E-mail: ajaykevat@gmail.com A. Kevat, Department of Respiratory Medicine, Monash Children's Hospital, 246 Clayton Road, Clayton, Melbourne, VIC 3168, Australia. E-mail: ajaykevat@gmail.com | en |
dc.description.publicationstatus | Embase | en |
dc.rights.statement | Copyright 2020 Elsevier B.V., All rights reserved. | en |
dc.subect.keywords | Artificial intelligence Auscultation Child Respiratory sounds Stethoscopes | en |
dc.identifier.authoremail | Kevat A.; ajaykevat@gmail.com | en |
item.openairetype | Article | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | Monash Doctors Education | - |
crisitem.author.dept | Paediatric - Respiratory and Sleep (Melbourne Children's Sleep Centre) | - |
Appears in Collections: | Articles |
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