Efficacy of artificial intelligence in the categorisation of paediatric pneumonia on chest radiographs: A systematic review

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dc.contributor.authorField, E. L.
dc.contributor.authorTam, W.
dc.contributor.authorMoore, N.
dc.contributor.authorMcEntee, Mark F.
dc.date.accessioned2023-07-13T13:06:20Z
dc.date.available2023-07-13T13:06:20Z
dc.date.issued2023
dc.description.abstractThis study aimed to systematically review the literature to synthesise and summarise the evidence surrounding the efficacy of artificial intelligence (AI) in classifying paediatric pneumonia on chest radiographs (CXRs). Following the initial search of studies that matched the pre-set criteria, their data were extracted using a data extraction tool, and the included studies were assessed via critical appraisal tools and risk of bias. Results were accumulated, and outcome measures analysed included sensitivity, specificity, accuracy, and area under the curve (AUC). Five studies met the inclusion criteria. The highest sensitivity was by an ensemble AI algorithm (96.3%). DenseNet201 obtained the highest level of specificity and accuracy (94%, 95%). The most outstanding AUC value was achieved by the VGG16 algorithm (96.2%). Some of the AI models achieved close to 100% diagnostic accuracy. To assess the efficacy of AI in a clinical setting, these AI models should be compared to that of radiologists. The included and evaluated AI algorithms showed promising results. These algorithms can potentially ease and speed up diagnosis once the studies are replicated and their performances are assessed in clinical settings, potentially saving millions of lives. � 2023 by the authors.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationField, E. L., Tam, W., Moore, N. and McEntee, M. (2023) 'Efficacy of artificial intelligence in the categorisation of paediatric pneumonia on chest radiographs: A Systematic Review', Children,�10(3), 576 (13pp). doi: 10.3390/children10030576en
dc.identifier.doi10.3390/children10030576
dc.identifier.issn22279067
dc.identifier.issued3
dc.identifier.journaltitleChildrenen
dc.identifier.urihttps://hdl.handle.net/10468/14724
dc.identifier.volume10
dc.language.isoenen
dc.publisherMDPIen
dc.rights� 2023, the Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial Intelligence (AI)en
dc.subjectChest radiographen
dc.subjectComputer-aided detection (CAD)en
dc.subjectDeep learning (DL)en
dc.subjectPaediatric pneumoniaen
dc.titleEfficacy of artificial intelligence in the categorisation of paediatric pneumonia on chest radiographs: A systematic reviewen
dc.typeArticle (peer-reviewed)en
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