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| Name | Class |
|---|---|
| Gleamer | INDUSTRY |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Readers/participants | Reader Selection: 18 readers will be selected from the following five clinical specialty groups (3 readers each):
And from the following level of seniority/experience:
Each specialty reader group will include 1 reader at each level of experience. Readers will be recruited from across 5 NHS organisations which comprise the Thames Valley Emergency Medicine Research Network (www.TaVERNresearch.org):
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| Ground truthers | Two consultant musculoskeletal radiologists. A third senior musculoskeletal radiologist's opinion (>20 years experience) will undertake arbitration. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Cases reading | Other | The reading will be done remotely via the Report and Image Quality Control site (www.RAIQC.com), an online platform allowing medical imaging viewing and reporting. Participants can work from any location, but the work must be done from a computer with internet access. For avoidance of doubt, the work cannot be performed from a phone or tablet. The project is divided into two phases and participants are required to complete both phases. The estimated total involvement in the project is up to 20-24 hours. Phase 1: Time allowed: 2 weeks - Participants must review 500 X-rays and express a clinical opinion through a structured reporting template (multiple choice, no open text required). Rest/washout period - Time allowed: 4 weeks, to mitigate the effects of recall bias. Phase 2 - Time allowed: 2 weeks - Review 500 X-rays together with an AI report for each case and express their clinical opinion through the same structured reporting template used in Phase 1. |
| Measure | Description | Time Frame |
|---|---|---|
| Performance of AI algorithm: sensitivity | Evaluation of the Gleamer Boneview algorithm will be performed comparing it to the reference standard in order to determine sensitivity. | During 4 weeks of reading time |
| Performance of AI algorithm: specificity | Evaluation of the Gleamer Boneview will be performed comparing it to the reference standard in order to determine specificity. | During 4 weeks of reading time |
| Performance of AI algorithm: Area under the ROC Curve (AU ROC) | Evaluation of the Gleamer Boneview algorithm will be performed comparing it to the reference standard. Continuous probability score from the algorithm will be utilised for the ROC analyses, while binary classification results with a predefined operating cut-off will be used for evaluation of sensitivity, specificity, positive predictive value, and negative predictive value. | During 4 weeks of reading time |
| Performance of readers with and without AI assistance: Sensitivity | The study will include two sessions (with and without AI overlay), with all 18 readers reviewing all 500 XR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. | During 4 weeks of reading time |
| Performance of readers with and without AI assistance: Specificity | The study will include two sessions (with and without AI overlay), with all 18 readers reviewing all 500 XR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. | During 4 weeks of reading time |
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Inclusion Criteria:
Exclusion Criteria:
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Emergency medicine doctors, trauma and orthopaedic surgeons, emergency nurse practitioners, physiotherapists, general radiologists and radiographers reviewing X-rays as part of their routine clinical practice, currently working in the National Health Service (NHS).
Readers will be recruited from across 5 NHS organisations which comprise the Thames Valley Emergency Medicine Research Network (www.TaVERNresearch.org):
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Oxford University Hospitals NHS Foundation Trust | Oxford | Oxfordshire | OX3 9DU | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31801474 | Background | 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. | |
| 18192607 | Background | 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. |
| Label | URL |
|---|---|
| 3\. Clinical negligence claims in Emergency Departments in England. Report 2 of 3: Missed fractures. NHS Resolution. March 2022 | View source |
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| ID | Term |
|---|---|
| D050723 | Fractures, Bone |
| D004204 | Joint Dislocations |
| D000069076 | Fractures, Multiple |
| D005596 | Fractures, Closed |
| D005597 | Fractures, Open |
| ID | Term |
|---|---|
| D014947 | Wounds and Injuries |
| D007592 | Joint Diseases |
| D009140 | Musculoskeletal Diseases |
| D009104 | Multiple Trauma |
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| Ground truthing | Other | Two consultant musculoskeletal radiologists will independently review the images to establish the 'ground truth' findings on the XRs, where a consensus is reached this will then be used as the reference standard. In the case of disagreement, a third senior musculoskeletal radiologist's opinion (>20 years experience) will undertake arbitration. A difficulty score will be assigned to each abnormality by the ground truthers using a 4-point Likert scale (1 being easy/obvious to 4 being hard/poorly visualised). |
|
| Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC) |
The study will include two sessions (with and without AI overlay), with all 18 readers reviewing all 500 XR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. |
| During 4 weeks of reading time |
| Reader speed with vs without AI assistance. | Mean time taken to review a XR, with vs without AI assistance. | During 4 weeks of reading time |
| 26913322 | Background | 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/ |
| 32064195 | Background | 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. |
| 23726985 | Background | 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. |
| 31754742 | Background | 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. |
| 33856519 | Background | 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. |
| 33944629 | Background | 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. |
| 17409321 | Background | 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. |
| 30318264 | Background | 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. |
| 31005540 | Background | 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. |
| 35166584 | Background | 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. |
| 32351258 | Background | 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. |
| 39237277 | Derived | Novak A, Hollowday M, Espinosa Morgado AT, Oke J, Shelmerdine S, Woznitza N, Metcalfe D, Costa ML, Wilson S, Kiam JS, Vaz J, Limphaibool N, Ventre J, Jones D, Greenhalgh L, Gleeson F, Welch N, Mistry A, Devic N, Teh J, Ather S. Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study. BMJ Open. 2024 Sep 5;14(9):e086061. doi: 10.1136/bmjopen-2024-086061. |
| 11\. The NICE Evidence Standards Framework for digital health and care technologies. (ECD7) Last Updated: 9 August | View source |