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dc.contributor.authorFowler, G. E.
dc.contributor.authorMacefield, R. C.
dc.contributor.authorHardacre, C.
dc.contributor.authorCallaway, M. P.
dc.contributor.authorSmart, N. J.
dc.contributor.authorBlencowe, N. S.
dc.identifier.citationBMJ Open. 2021 Oct 20;11(10):e054411. doi: 10.1136/bmjopen-2021-054411.
dc.description.abstractINTRODUCTION: The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. METHODS AND ANALYSIS: A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines. ETHICS AND DISSEMINATION: No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021237249.
dc.rights© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.
dc.subject*Artificial Intelligence
dc.subjectCross-Sectional Studies
dc.subject*Diagnostic Imaging
dc.subjectSystematic Reviews as Topic
dc.subject*computed tomography
dc.subject*diagnostic radiology
dc.subject*magnetic resonance imaging
dc.titleArtificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review
dc.typeJournal Article
dc.identifier.journalBMJ open
dc.description.noteThe article is available via Open Access. Click on the 'Additional link' above to access the full-text.
dc.description.admin-notePublished version, accepted version, submitted version

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© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.
Except where otherwise noted, this item's license is described as © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.