Show simple item record

dc.contributor.authorFowler, G.E.
dc.contributor.authorBlencowe, N.S.
dc.contributor.authorHardacre, C.
dc.contributor.authorCallaway, M.P.
dc.contributor.authorSmart, N.J.
dc.contributor.authorMacefield, R.C.
dc.date.accessioned2022-04-21T09:40:22Z
dc.date.available2022-04-21T09:40:22Z
dc.date.issued2022-02-28
dc.identifier.doi10.1093/bjs/znac040.015
dc.identifier.urihttps://rde.dspace-express.com/handle/11287/622446
dc.description.abstractMedical imaging is important for diagnostic, prognostic, and management decisions. It is reliant on an increasingly limited number of interpreters. A developing interest has explored how artificial intelligence (AI) research in medical imaging can support clinicians and provide greater efficiency in clinical care. Reviews exist for thoracic and endoscopic imaging, but one is lacking for the abdominopelvic cavity. This could benefit several specialities which use this modality of imaging to guide their clinical decision making. This systematic review examines and critically appraises the application of AI models to identify surgical pathology from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) to identify relevant studies were performed, adhering to the PRISMA-DTA guidelines. Study characteristics and outcomes assessing diagnostic performance were extracted. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines.10 retrospective studies were included, comprising 3,096 and 1,432 patients for AI training and test sets, respectively. There was diversity in the speciality, intention of the AI applications and the reporting, which was unstandardised. Diagnostic performance of models varied (range: 70–95% sensitivity, 73.7%-98% specificity). Only one study used a comparator, in which AI (AUC=0.920) outperformed both senior and junior radiologists (AUC=0.791 and 0.780, respectively).AI application in this field is diverse and adherence to new and developing reporting guidelines is warranted. With finite healthcare resources and funding, future endeavours may benefit from prioritising clinical need, rather than scientific inquiry.
dc.language.isoeng
dc.publisherWiley
dc.relation.urlhttps://academic.oup.com/bjs/article/109/Supplement_1/znac040.015/6539425
dc.rights© 2022, Oxford University Press
dc.title157 Diagnostic Test Accuracy of Artificial Intelligence Models Used in Cross-Sectional Radiological Imaging of Surgical Pathology in the Abdominopelvic Cavity: A Systematic Review
dc.typeConference Paper
dc.identifier.journalBritish Journal of Surgery
dc.description.noteRD&E staff can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted.
dc.type.versionepublish
dc.description.admin-notePublished version, accepted version (12 month embargo), submitted version
dc.citation.volume109
dc.citation.issueSupplement_1


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record