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Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals

dc.contributor.authorWilliams, S.C.
dc.contributor.authorZhou, J.
dc.contributor.authorMuirhead, W.R.
dc.contributor.authorKhan, D.Z.
dc.contributor.authorKoh, C.H.
dc.contributor.authorAhmed, R.
dc.contributor.authorFunnell, J.P.
dc.contributor.authorHanrahan, J.G.
dc.contributor.authorAli, A.M.
dc.contributor.authorGhosh, S.
dc.contributor.authorSarıdoğan, T.
dc.contributor.authorValetopoulou, A.
dc.contributor.authorGrover, P.
dc.contributor.authorStoyanov, D.
dc.contributor.authorMurphy, M.
dc.contributor.authorMazomenos, E.B.
dc.contributor.authorMarcus, H.J.
dc.contributor.buuauthorSarıdoğan, Tarık
dc.contributor.departmentTıp Fakültesi
dc.contributor.departmentAcil Tıp Ana Bilim Dalı
dc.contributor.scopusid59405270100
dc.date.accessioned2025-05-12T22:32:12Z
dc.date.issued2024-01-01
dc.description.abstractObjective: This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team’s ability to detect cerebral aneurysms with and without AI-assistance. Background: Modern microscopic surgery enables the capture of operative video data on an unforeseen scale. Advances in computer vision, a branch of artificial intelligence (AI), have enabled automated analysis of operative video. These advances are likely to benefit clinicians, healthcare systems, and patients alike, yet such benefits are yet to be realised. Methods: In a cross-sectional comparative study, neurosurgeons, anaesthetists, and operating room (OR) nurses, all at varying stages of training and experience, reviewed still frames of aneurysm clipping operations and labelled frames as ‘aneurysm not in frame’ or ‘aneurysm in frame’. Frames then underwent analysis by the AI platform. A second round of data collection was performed whereby the neurosurgical team had AI-assistance. Accuracy of aneurysm detection was calculated for human only, AI only, and AI-assisted human groups. Results: 5,154 individual frame reviews were collated from 338 healthcare professionals. Healthcare professionals correctly labelled 70% of frames without AI assistance, compared to 78% with AI-assistance (OR 1.77, p<0.001). Neurosurgical Attendings showed the greatest improvement, from 77% to 92% correct predictions with AI-assistance (OR 4.24, p=0.003). Conclusion: AI-assisted human performance surpassed both human and AI alone. Notably, across healthcare professionals surveyed, frame accuracy improved across all subspecialties and experience levels, particularly among the most experienced healthcare professionals. These results challenge the prevailing notion that AI primarily benefits junior clinicians, highlighting its crucial role throughout the surgical hierarchy as an essential component of modern surgical practice.
dc.description.sponsorshipNational Institute for Health and Care Research
dc.description.sponsorshipUniversity College London Hospitals Biomedical Research Centre
dc.description.sponsorshipEngineering and Physical Sciences Research Council -- NS/A000050/1
dc.description.sponsorshipEngineering and Physical Sciences Research Counci
dc.description.sponsorshipWellcome Trust -- 203145Z/16/Z
dc.description.sponsorshipWellcome Trust
dc.identifier.doi10.1097/SLA.0000000000006577
dc.identifier.issn0003-4932
dc.identifier.scopus2-s2.0-85208673994
dc.identifier.urihttps://hdl.handle.net/11452/51350
dc.indexed.scopusScopus
dc.language.isoen
dc.publisherWolters Kluwer Health
dc.relation.journalAnnals of Surgery
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectVascular Neurosurgery
dc.subjectNeurosurgery
dc.subjectMachine Learning
dc.subjectComputer Vision
dc.subjectArtificial Intelligence
dc.subjectAneurysm
dc.subject.scopusAI Integration in Surgical Video Analysis
dc.titleArtificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentTıp Fakültesi/ Acil Tıp Ana Bilim Dalı
local.indexed.atScopus

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