AI Assisted Detection of Fractures on X-Rays (FRACT-AI)

NCT06130397 | Completed

• 1 other identifier: 310995-C
• Study Type: observational
• Target: 21
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Fracture; Bone Fracture; Dislocation; Fracture Multiple; Fractures, Closed; Fractures, Open

Keywords

radiology; emergency medicine; artificial intelligence; XRays; Fractures

Outcome Measures

Primary Outcomes (7)

• 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

• 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

Study Arms (2)

Readers/participants

Reader Selection: 18 readers will be selected from the following five clinical specialty groups (3 readers each): * Emergency Medicine * Trauma and Orthopaedic Surgery * Emergency Nurse Practitioners * Physiotherapy * General Radiology * Radiographers And from the following level of seniority/experience: * Consultant/Senior/Equivalent - \>10yrs experience * Middle Grade/Registrar/Equivalent - 5-10yrs experience * Junior Grade/Senior House Officer/Equivalent - \<5yrs 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): * Oxford University Hospitals NHS Foundation Trust * Royal Berkshire NHS Foundation Trust * Buckinghamshire Healthcare NHS Trust * Frimley Health NHS Foundation Trust * Milton Keynes University Hospital NHS Foundation Trust

Other: Cases reading

Ground truthers

Two consultant musculoskeletal radiologists. A third senior musculoskeletal radiologist's opinion (\>20 years experience) will undertake arbitration.

Other: Ground truthing

Interventions

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.

Readers/participants

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).

Ground truthers

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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): * Oxford University Hospitals NHS Foundation Trust * Royal Berkshire NHS Foundation Trust * Buckinghamshire Healthcare NHS Trust * Frimley Health NHS Foundation Trust * Milton Keynes University Hospital NHS Foundation Trust

You may qualify if:

• 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).

You may not qualify if:

• Non-radiology physicians with previous formal postgraduate XR reporting training.
• Non-radiology physicians with previous career in radiology

Sponsors & Collaborators

• Oxford University Hospitals NHS Trust: lead
• Gleamer: collaborator

Study Sites (1)

Oxford University Hospitals NHS Foundation Trust

Oxford, Oxfordshire, OX3 9DU, United Kingdom

Location

Related Publications (14)

• 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.

  PMID: 31801474 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.

  PMID: 18192607 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/

  PMID: 26913322 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.

  PMID: 32064195 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.

  PMID: 23726985 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.

  PMID: 31754742 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.

  PMID: 33856519 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.

  PMID: 33944629 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.

  PMID: 17409321 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.

  PMID: 30318264 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.

  PMID: 31005540 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.

  PMID: 35166584 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.

  PMID: 32351258 BACKGROUND

• 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.

  PMID: 39237277 DERIVED

Related Links

• 3\. Clinical negligence claims in Emergency Departments in England. Report 2 of 3: Missed fractures. NHS Resolution. March 2022
• 11\. The NICE Evidence Standards Framework for digital health and care technologies. (ECD7) Last Updated: 9 August

MeSH Terms

Conditions

Fractures, Bone; Joint Dislocations; Fractures, Multiple; Fractures, Closed; Fractures, Open

Condition Hierarchy (Ancestors)

Wounds and Injuries; Joint Diseases; Musculoskeletal Diseases; Multiple Trauma

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Primary Investigator

Study Record Dates

• First Submitted: November 8, 2023
• First Posted: November 14, 2023
• Study Start: February 8, 2024
• Primary Completion: October 31, 2024
• Study Completion: June 1, 2025
• Last Updated: November 24, 2025

Record last verified: 2025-11

Locations

GB (1)