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

Fracture Clinical Trial

— FRACT-AI  

Official title:

FRACT-AI: Evaluating the Impact of Artificial Intelligence-Enhanced Image Analysis on the Diagnostic Accuracy of Frontline Clinicians in the Detection of Fractures on Plain X-Ray

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06130397
• Other study ID #: 310995-C
• Status: Recruiting
• Start date: February 8, 2024
• Est. completion date: December 2024

Study information

• Verified date: April 2024
• Source: Oxford University Hospitals NHS Trust
• Contact: Alex Novak, MSc
• Phone: +44 7944653979
• Email: alex.novak[@]ouh.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 21
• Est. completion date: December 2024
• Est. primary completion date: October 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• 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).

Exclusion Criteria:

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

Related Conditions & MeSH terms

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

Intervention

Other:
• Cases reading
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.
• Ground truthing
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).

Locations

Country	Name	City	State
United Kingdom	Oxford University Hospitals NHS Foundation Trust	Oxford	Oxfordshire
United Kingdom	Oxford University Hospitals NHS Foundation Trust	Oxford	Oxfordshire

Sponsors (2)

Lead Sponsor	Collaborator
Oxford University Hospitals NHS Trust	Gleamer

Country where clinical trial is conducted

United Kingdom, 

References & Publications (13)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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/ — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 13 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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

External Links

•   11. The NICE Evidence Standards Framework for digital health and care technologies. (ECD7) Last Updated: 9 August
•   12. Emergency Medicine Refresh Top 10
•   3. Clinical negligence claims in Emergency Departments in England. Report 2 of 3: Missed fractures. NHS Resolution. March 2022
•   Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology