FractureFRACT-AINCT06130397
AI Assisted Detection of Fractures on X-Rays (FRACT-AI)
An observational study of Fracture and Bone Fracture, run by Oxford University Hospitals NHS Trust at 1 site in United Kingdom.
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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Fracture, Bone Fracture, Dislocation
- What is being tested?
- Cases reading and Ground truthing
- Type of treatment
- Pill or oral medicine
- How many people
- 21 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 8 Feb 2024to 1 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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
Who may be able to join
- 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).
Who cannot join
- Non-radiology physicians with previous formal postgraduate XR reporting training.
- Non-radiology physicians with previous career in radiology
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Oxford University Hospitals NHS Foundation Trust
Oxford, Oxfordshire, OX3 9DU
About this study
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
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
- Being tested
- Cases reading, Ground truthing
- Keywords
- radiology, emergency medicine, artificial intelligence, XRays, Fractures
- Medical subject headings
- Fractures, Bone, Joint Dislocations, Fractures, Multiple, Fractures, Closed, Fractures, Open
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cases readingOther
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 truthingOther
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).
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Other: Cases reading | |
| Ground truthers | Two consultant musculoskeletal radiologists. A third senior musculoskeletal radiologist's opinion (>20 years experience) will undertake arbitration. Receives: Other: Ground truthing |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: During 4 weeks of reading time
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Nov 2023
- Study start8 Feb 2024
- Primary completion31 Oct 2024
- Study completion1 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for FractureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 21 participants (actual)
Sponsor and investigators
- Lead sponsor
- Oxford University Hospitals NHS Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Gleamer
- Responsible party
- Principal investigator, Alex Novak, Oxford University Hospitals NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06130397
- Sponsor's protocol ID
- 310995-C
- Acronym
- FRACT-AI
- Status last verified
- Nov 2025
- First submitted
- 8 Nov 2023
- Last updated
- 24 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 31801474Donaldson 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.
PubMed 18192607National 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/
PubMed 26913322Blazar 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.
PubMed 32064195Snaith 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.
PubMed 23726985York 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.
PubMed 31754742van 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.
PubMed 33856519Duron 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.
PubMed 33944629Fenton 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.
PubMed 17409321Chilamkurthy 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.
PubMed 30318264Patel 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.
PubMed 31005540Obuchowski 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.
PubMed 35166584Smith 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.
PubMed 32351258Novak 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.
PubMed 39237277
Study at a glance
From the registry record, last updated by the sponsor on 24 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06130397
- Acronym / sponsor ID
- FRACT-AI / 310995-C
- Conditions
- Fracture, Bone Fracture, Dislocation, Fracture Multiple, Fractures, Closed, Fractures, Open
- Keywords
- radiology, emergency medicine, artificial intelligence, XRays, Fractures
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 21 participants (actual)
- Lead sponsor
- Oxford University Hospitals NHS TrustUniversity, hospital or other
- Collaborators
- Gleamer
- Study start
- 8 Feb 2024 (actual)
- Primary completion
- 31 Oct 2024 (actual)
- Study completion
- 1 Jun 2025 (actual)
- First posted
- 14 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Readers/participants | – | Other: Cases readingReader 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 |
| Ground truthers | – | Other: Ground truthingTwo consultant musculoskeletal radiologists. A third senior musculoskeletal radiologist's opinion (>20 years experience) will undertake arbitration. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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
Key inclusion criteria (2 listed)
- 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).
Key exclusion criteria (2 listed)
- Non-radiology physicians with previous formal postgraduate XR reporting training.
- Non-radiology physicians with previous career in radiology
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Cases readingThe 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. | Other | Readers/participants |
| Ground truthingTwo 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). | Other | Ground truthers |
Primary outcome
Performance of AI algorithm: sensitivity
Time frame: During 4 weeks of reading time
Evaluation of the Gleamer Boneview algorithm will be performed comparing it to the reference standard in order to determine sensitivity.
Performance of AI algorithm: specificity
Time frame: During 4 weeks of reading time
Evaluation of the Gleamer Boneview will be performed comparing it to the reference standard in order to determine specificity.
Performance of AI algorithm: Area under the ROC Curve (AU ROC)
Time frame: During 4 weeks of reading time
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.
Performance of readers with and without AI assistance: Sensitivity
Time frame: During 4 weeks of reading time
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.
Performance of readers with and without AI assistance: Specificity
Time frame: During 4 weeks of reading time
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.
Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC)
Time frame: During 4 weeks of reading time
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.
Reader speed with vs without AI assistance.
Time frame: During 4 weeks of reading time
Mean time taken to review a XR, with vs without AI assistance.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Nov 2025.
- Background: PubMed 31801474Hussain 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.
- Background: PubMed 18192607Donaldson 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.
- Background: PubMed 26913322National 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/
- Background: PubMed 32064195Blazar 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.
- Background: PubMed 23726985Snaith 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.
- Background: PubMed 31754742York 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.
- Background: PubMed 33856519van 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.
- Background: PubMed 33944629Duron 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.
- Background: PubMed 17409321Fenton 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.
- Background: PubMed 30318264Chilamkurthy 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.
- Background: PubMed 31005540Patel 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.
- Background: PubMed 35166584Obuchowski 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.
- 3\. Clinical negligence claims in Emergency Departments in England. Report 2 of 3: Missed fractures. NHS Resolution. March 2022Link provided by the sponsor.
- 11\. The NICE Evidence Standards Framework for digital health and care technologies. (ECD7) Last Updated: 9 AugustLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fracture and Bone Fracture, run by Oxford University Hospitals NHS Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of AI algorithm: sensitivity (time frame: During 4 weeks of reading time). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06130397, which also lists 14 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Nov 2025; AzTrials last refreshed its copy on 7 Nov 2026.