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Fractures, Bone Fractures, Multiple Fractures, Closed Fractures, Open
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This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838). This work aims to evaluate the impact of an Artificial Intelligence (AI)-enhanced algorithm called Boneview on the diagnostic accuracy of clinicians in the detection of fractures on plain XR (X-Ray). The study will create a dataset of 500 plain X-Rays involving standard images of all bones other than the skull and cervical spine, with 50% normal cases and 50% containing fractures. A reference 'ground truth' for each image to confirm the presence or absence of a fracture will be established by a senior radiologist panel. This dataset will then be inferenced by the Gleamer Boneview algorithm to identify fractures. Performance of the algorithm will be compared against the reference standard. The study will then undertake a Multiple-Reader Multiple-Case study in which clinicians interpret all images without AI and then subsequently with access to the output of the AI algorithm. 18 clinicians will be recruited as readers with 3 from each of six distinct clinical groups: Emergency Medicine, Trauma and Orthopedic Surgery, Emergency Nurse Practitioners, Physiotherapy, Radiology and Radiographers, with three levels of seniority in each group. Changes in reporting accuracy (sensitivity, specificity), confidence, and speed of readers in two sessions will be compared. The results will be analyzed in a pooled analysis for all readers as well as for the following subgroups: Clinical role, Level of seniority, Pathological finding, Difficulty of image. The study will demonstrate the impact of an AI interpretation as compared with interpretation by clinicians, and as compared with clinicians using the AI as an adjunct to their interpretation. The study will represent a range of professional backgrounds and levels of experience among the clinical element. The study will use plain film x-rays that will represent a range of anatomical views and pathological presentations, however x-rays will present equal numbers of pathological and non-pathological x-rays, giving equal weight to assessment of specificity and sensitivity. Ethics approval has already been granted, and the study will be disseminated through publication in peer-reviewed journals and presentation at relevant conferences.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
"Hussain F, Cooper A, Carson-Stevens A, Donaldson L, Hibbert P, Hughes T, Edwards A. Diagnostic error in the emergency department: learning from national patient safety incident report analysis. BMC Emerg Med. 2019 Dec 4;19(1):77. doi: 10.1186/s12873-019-0289-3."; "31801474"; "Donaldson LJ, Reckless IP, Scholes S, Mindell JS, Shelton NJ. The epidemiology of fractures in England. J Epidemiol Community Health. 2008 Feb;62(2):174-80. doi: 10.1136/jech.2006.056622."; "18192607"; "National Clinical Guideline Centre (UK). Fractures (Non-Complex): Assessment and Management. London: National Institute for Health and Care Excellence (NICE); 2016 Feb. Available from http://www.ncbi.nlm.nih.gov/books/NBK344251/"; "26913322"; "Blazar E, Mitchell D, Townzen JD. Radiology Training in Emergency Medicine Residency as a Predictor of Confidence in an Attending. Cureus. 2020 Jan 9;12(1):e6615. doi: 10.7759/cureus.6615."; "32064195"; "Snaith B, Hardy M. Emergency department image interpretation accuracy: The influence of immediate reporting by radiology. Int Emerg Nurs. 2014 Apr;22(2):63-8. doi: 10.1016/j.ienj.2013.04.004. Epub 2013 May 30."; "23726985"; "York TJ, Jenkins PJ, Ireland AJ. Reporting Discrepancy Resolved by Findings and Time in 2947 Emergency Department Ankle X-rays. Skeletal Radiol. 2020 Apr;49(4):601-611. doi: 10.1007/s00256-019-03317-7. Epub 2019 Nov 21."; "31754742"; "van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15."; "33856519"; "Duron L, Ducarouge A, Gillibert A, Laine J, Allouche C, Cherel N, Zhang Z, Nitche N, Lacave E, Pourchot A, Felter A, Lassalle L, Regnard NE, Feydy A. Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. Radiology. 2021 Jul;300(1):120-129. doi: 10.1148/radiol.2021203886. Epub 2021 May 4."; "33944629"; "Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D'Orsi C, Berns EA, Cutter G, Hendrick RE, Barlow WE, Elmore JG. Influence of computer-aided detection on performance of screening mammography. N Engl J Med. 2007 Apr 5;356(14):1399-409. doi: 10.1056/NEJMoa066099."; "17409321"; "Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018 Dec 1;392(10162):2388-2396. doi: 10.1016/S0140-6736(18)31645-3. Epub 2018 Oct 11."; "30318264"; "Patel MR, Norgaard BL, Fairbairn TA, Nieman K, Akasaka T, Berman DS, Raff GL, Hurwitz Koweek LM, Pontone G, Kawasaki T, Sand NPR, Jensen JM, Amano T, Poon M, Ovrehus KA, Sonck J, Rabbat MG, Mullen S, De Bruyne B, Rogers C, Matsuo H, Bax JJ, Leipsic J. 1-Year Impact on Medical Practice and Clinical Outcomes of FFRCT: The ADVANCE Registry. JACC Cardiovasc Imaging. 2020 Jan;13(1 Pt 1):97-105. doi: 10.1016/j.jcmg.2019.03.003. Epub 2019 Mar 17."; "31005540"; "Obuchowski NA, Bullen J. Multireader Diagnostic Accuracy Imaging Studies: Fundamentals of Design and Analysis. Radiology. 2022 Apr;303(1):26-34. doi: 10.1148/radiol.211593. Epub 2022 Feb 15."; "35166584"; "Smith BJ, Hillis SL. Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities. Proc SPIE Int Soc Opt Eng. 2020 Feb;11316:113160K. doi: 10.1117/12.2549075. Epub 2020 Mar 16."; "32351258"
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The study is sponsored by Oxford University Hospitals NHS Trust and is in collaboration with Gleamer.
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