Bone FracturesFAIRNCT06754137
AI-Assisted Fracture Detection in Emergency Radiography
A study testing BoneView AI-Assisted Radiograph Interpretation in people with Bone Fractures, run by Salzburger Landeskliniken at 3 sites in 2 countries.
Trial summary
This study evaluates whether artificial intelligence (AI) can support doctors who interpret X-rays for suspected fractures in emergency care. In the participating hospitals, X-rays are usually interpreted first by the frontline treating physician, while the formal radiology report is generally available later and not… Read more about this study
- Who is it for?
- Ages 2 years and older
- Conditions the study is for
- Bone Fractures
- What is being tested?
- BoneView AI-Assisted Radiograph Interpretation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,667 participants
- Where
- 3 sitesin 2 countries
- Study dates
- 1 Oct 2025to 30 Apr 2026
- Phase
- Interventional
- 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 2 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Age 2 years or older.
- Presentation to the emergency department following trauma or relevant joint complaint requiring plain radiographic imaging.
- Injury involving a single anatomical region within the supported anatomical scope of the AI system.
- Written informed consent provided by the participant or legally authorized representative, with age-appropriate assent where applicable.
Who cannot join
- Age younger than 2 years.
- Injuries involving multiple anatomical regions.
- Head or cervical spine injuries.
- Previous radiographic imaging or medical assessment for the same injury before the index presentation.
- Contraindication to X-ray imaging, including pregnancy.
- Lack of informed consent.
- Reduced image quality was not an exclusion criterion. Multiple fractures within the same anatomical injury region were eligible.
Trial locations and contacts
3 sites in 2 countries.
3 sites in 2 countries
Austria2 sites
Landesklinik Hallein, Salzburger Landeskliniken
Hallein, 5400
University Hospital Salzburg, Salzburger Landeskliniken
Salzburg, 5020
Germany1 site
University Hosptial Nuremberg, Klinikum Nürnberg
Nuremberg, 90471
About this study
Official title: Artificial Intelligence-Assisted Fracture Detection in Emergency Radiography: A Multicentre Pragmatic Randomised Controlled Trial
This study evaluates whether artificial intelligence (AI) can support doctors who interpret X-rays for suspected fractures in emergency care.
In the participating hospitals, X-rays are usually interpreted first by the frontline treating physician, while the formal radiology report is generally available later and not before the patient leaves the emergency department. AI may therefore provide an immediate additional assessment while clinical decisions are being made.
Read more
Patients were randomly assigned to one of two groups. In the AI-assisted group, physicians interpreted the X-rays with support from an AI system. In the control group, physicians interpreted the same type of X-rays without AI support. All final diagnoses and treatment decisions remained with the treating physician.
The main question is whether AI assistance affects the time from emergency department triage to discharge. The study also evaluates whether AI influences physician diagnostic confidence, the use of additional imaging, missed fractures, and diagnostic accuracy.
The study included patients aged 2 years or older presenting with an isolated musculoskeletal injury or relevant joint complaint requiring X-ray imaging. No additional imaging or treatment was required solely because of study participation.
Read the detailed description
The FAIR (Fracture detection with AI in emergency Radiography) Trial was a prospective, international, multicentre, pragmatic randomised controlled trial evaluating the clinical impact of artificial intelligence (AI)-assisted interpretation of musculoskeletal radiographs in routine emergency care. The study was conducted across four clinical units at three hospitals in Austria and Germany.
Clinical workflow and randomisation
In the participating departments, radiographs were interpreted by frontline orthopaedic trauma or paediatric physicians, who made immediate diagnostic and treatment decisions. Formal radiology reports were generally available later. The intervention therefore evaluated AI as an additional decision-support tool rather than as a replacement for physician interpretation. Patient encounters were individually randomised using a single sequence generated by the trial statistician. Allocation was concealed using folded, sequentially numbered study forms and revealed after radiography. Patients were then directed to designated AI-assisted or control treatment areas. All clinical decisions remained the responsibility of the treating physician.
AI implementation
The intervention used BoneView version 2.3.8 (Gleamer, Paris, France), which provided visual annotations and classifications for fractures, focal bone lesions, dislocations, and joint effusions. The software version remained unchanged throughout recruitment. At Salzburg and Hallein, BoneView was delivered through Aidoc aiOS version 3.24.0; at Nuremberg, it was integrated through Visage version 7.1.19. Participating physicians received standardised training before using the system. AI output was documented on study case-report forms, although detailed software telemetry on actual viewing behaviour was unavailable.
Protocol evolution
During recruitment, an exploratory unblinded analysis in October 2025 examined prospectively collected clinical and workflow outcomes. Following methodological feedback during the Austrian Science Fund review process in March 2026, the outcome hierarchy for the fracture-focused clinical-effectiveness analysis was revised in consultation with the trial statistician, prioritising time from emergency department triage to discharge over diagnostic accuracy. The amendment did not alter randomisation, clinical procedures, or data collection. The originally registered cost-efficiency outcome was discontinued because the available data did not permit a reliable health-economic evaluation.
Reference standard
For fracture-related diagnostic analyses, a radiologist experienced in musculoskeletal imaging and a senior orthopaedic trauma surgeon reviewed the index radiographs and available clinical information. Disagreements were resolved by consensus or, when necessary, by a third expert. The assessment distinguished fractures visible on the original radiographs from fractures detectable only on subsequent imaging.
- Conditions
- Bone Fractures
- Being tested
- BoneView AI-Assisted Radiograph Interpretation
- Keywords
- Artificial Intelligence, Fracture Detection, Emergency Care, AI-Assisted Diagnosis, Diagnostic Accuracy, Orthopedic Diagnostics, Emergency Radiography, Clinical Decision Support, Diagnostic Confidence, Emergency Department
- Medical subject headings
- Fractures, Bone, Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BoneView AI-Assisted Radiograph InterpretationDiagnostic test
BoneView version 2.3.8 (Gleamer, Paris, France) analyzes DICOM radiographs and provides real-time visual annotations and classifications for supported musculoskeletal abnormalities. Physicians in the intervention group could view fracture-related findings as well as other supported outputs, including dislocations, joint effusions, and focal bone lesions.
At University Hospital Salzburg and Regional Hospital Hallein, BoneView was delivered through the Aidoc aiOS platform (version 3.24.0). At University Hospital Nuremberg, BoneView was integrated directly into the local imaging workflow.
BoneView was used as a decision-support tool and did not replace physician interpretation or the subsequent formal radiology report.
| Study group | Role | What they receive |
|---|---|---|
| Standard Radiograph Interpretation Without AI | No intervention | Plain radiographs are interpreted by the frontline treating physician according to routine clinical practice without access to AI output. All diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. Formal radiology reports are generally available later and are not routinely available before completion of the emergency department encounter. |
| AI-Assisted Radiograph Interpretation | Experimental | Plain radiographs are interpreted by the frontline treating physician with access to real-time output from BoneView version 2.3.8 (Gleamer, Paris, France). The AI output is used as diagnostic decision support only; all final diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. Receives: Diagnostic Test: BoneView AI-Assisted Radiograph Interpretation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Time from Emergency Department Triage to Discharge
Time in minutes from formal emergency department triage to discharge during the index encounter, compared between the AI-assisted and control groups.
Measured: During the index emergency department encounter (day 1).
Secondary outcomes (4)
Missed fractures
Proportion of patient encounters with a fracture visible on the index radiographs, according to the expert-adjudicated reference standard, in which the fracture was not identified by the frontline treating physician during the initial diagnostic assessment. The reference standard is established by a radiologist and a senior orthopaedic trauma surgeon, with disagreements resolved by consensus or a third expert. Fractures identified on subsequent imaging but not visible on the original radiographs are not classified as missed radiographically visible fractures.
Measured: At the initial diagnostic assessment during the index emergency department encounter (day 1), with subsequent expert reference-standard adjudication.
Additional Diagnostic Imaging Requested
Proportion of patient encounters in which the treating physician requested additional diagnostic imaging during the index emergency department visit. The frequency of additional imaging requests is compared between the AI-assisted and control groups.
Measured: During the index emergency department encounter (day 1), following initial radiographic assessment.
Physician Diagnostic Confidence
The level of confidence reported by physicians in their diagnostic decisions, measured on a Likert scale (1-10). This will compare how confident physicians feel when using AI assistance versus relying solely on their expertise.
Measured: Measured immediately after the diagnosis
Diagnostic Accuracy of Fracture/Dislocation/Effusion/Bone Lesion Detection
The primary outcome measures the diagnostic accuracy of detecting broken bones/dislocations/effusions/bone lesions using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Diagnostic accuracy will be compared between the AI-assisted diagnostic approach and the standard physician-only approach. The gold standard for comparison will be determined by expert consensus based on independent review by a radiologist and an orthopedic specialist.
Measured: At the time of initial diagnosis, within 2 hours of patient presentation to the orthopedic emergency unit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Dec 2024
- Study start1 Oct 2025
- Primary completion30 Apr 2026
- Study completion30 Apr 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Bone FracturesStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants were individually randomized in a 1:1 ratio to AI-assisted radiograph interpretation or standard radiograph interpretation without AI assistance. A single global randomization sequence was generated by the trial statistician and used across all three participating centers.
- Masking
- None (open label)
- Enrolment
- 1,667 participants (actual)
Sponsor and investigators
- Lead sponsor
- Salzburger Landeskliniken
- Sponsor type
- University, hospital or other
- Collaborators
- Klinikum Nürnberg
- Responsible party
- Principal investigator, Martin Breitwieser, Salzburger Landeskliniken
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06754137
- Sponsor's protocol ID
- KI-FRACTURE_001_2024-11-27
- Acronym
- FAIR
- Status last verified
- Oct 2026
- First submitted
- 9 Dec 2024
- Last updated
- 6 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data underlying the results reported in the primary publication will be made available to qualified researchers upon reasonable written request. Shared data may include participant demographics, anatomical injury region, frontline physician diagnosis, AI findings, expert-adjudicated reference-standard findings, diagnostic confidence, additional imaging, and timing data used to derive the primary endpoint. Data will be shared only for scientifically justified research purposes following review and approval of the proposed project by the study investigators and execution of an appropriate data-sharing agreement. Data sharing will comply with applicable institutional requirements, the General Data Protection Regulation (GDPR), and relevant Austrian and German data-protection legislation.
Publications
Breitwieser M, Zirknitzer S, Poslusny K, Freude T, Scholsching J, Bodenschatz K, Wagner A, Hergan K, Schaffert M, Metzger R, Marko P. AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model. Diagnostics (Basel). 2025 Aug 21;15(16):2117. doi: 10.3390/diagnostics15162117.
PubMed 40870969
Study at a glance
From the registry record, last updated by the sponsor on 6 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Fracture Detection in Emergency Radiography: A Multicentre Pragmatic Randomised Controlled Trial
- ClinicalTrials.gov ID
- NCT06754137
- Acronym / sponsor ID
- FAIR / KI-FRACTURE_001_2024-11-27
- Conditions
- Bone Fractures
- Keywords
- Artificial Intelligence, Fracture Detection, Emergency Care, AI-Assisted Diagnosis, Diagnostic Accuracy, Orthopedic Diagnostics, Emergency Radiography, Clinical Decision Support
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,667 participants (actual)
- Lead sponsor
- Salzburger LandesklinikenUniversity, hospital or other
- Collaborators
- Klinikum Nürnberg
- Study start
- 1 Oct 2025 (actual)
- Primary completion
- 30 Apr 2026 (actual)
- Study completion
- 30 Apr 2026 (actual)
- First posted
- 31 Dec 2024 (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 evaluates whether artificial intelligence (AI) can support doctors who interpret X-rays for suspected fractures in emergency care.
In the participating hospitals, X-rays are usually interpreted first by the frontline treating physician, while the formal radiology report is generally available later and not before the patient leaves the emergency department. AI may therefore provide an immediate additional assessment while clinical decisions are being made.
Patients were randomly assigned to one of two groups. In the AI-assisted group, physicians interpreted the X-rays with support from an AI system. In the control group, physicians interpreted the same type of X-rays without AI support. All final diagnoses and treatment decisions remained with the treating physician.
The main question is whether AI assistance affects the time from emergency department triage to discharge. The study also evaluates whether AI influences physician diagnostic confidence, the use of additional imaging, missed fractures, and diagnostic accuracy.
The study included patients aged 2 years or older presenting with an isolated musculoskeletal injury or relevant joint complaint requiring X-ray imaging. No additional imaging or treatment was required solely because of study participation.
Detailed description
The FAIR (Fracture detection with AI in emergency Radiography) Trial was a prospective, international, multicentre, pragmatic randomised controlled trial evaluating the clinical impact of artificial intelligence (AI)-assisted interpretation of musculoskeletal radiographs in routine emergency care. The study was conducted across four clinical units at three hospitals in Austria and Germany.
Clinical workflow and randomisation
In the participating departments, radiographs were interpreted by frontline orthopaedic trauma or paediatric physicians, who made immediate diagnostic and treatment decisions. Formal radiology reports were generally available later. The intervention therefore evaluated AI as an additional decision-support tool rather than as a replacement for physician interpretation. Patient encounters were individually randomised using a single sequence generated by the trial statistician. Allocation was concealed using folded, sequentially numbered study forms and revealed after radiography. Patients were then directed to designated AI-assisted or control treatment areas. All clinical decisions remained the responsibility of the treating physician.
AI implementation
The intervention used BoneView version 2.3.8 (Gleamer, Paris, France), which provided visual annotations and classifications for fractures, focal bone lesions, dislocations, and joint effusions. The software version remained unchanged throughout recruitment. At Salzburg and Hallein, BoneView was delivered through Aidoc aiOS version 3.24.0; at Nuremberg, it was integrated through Visage version 7.1.19. Participating physicians received standardised training before using the system. AI output was documented on study case-report forms, although detailed software telemetry on actual viewing behaviour was unavailable.
Protocol evolution
During recruitment, an exploratory unblinded analysis in October 2025 examined prospectively collected clinical and workflow outcomes. Following methodological feedback during the Austrian Science Fund review process in March 2026, the outcome hierarchy for the fracture-focused clinical-effectiveness analysis was revised in consultation with the trial statistician, prioritising time from emergency department triage to discharge over diagnostic accuracy. The amendment did not alter randomisation, clinical procedures, or data collection. The originally registered cost-efficiency outcome was discontinued because the available data did not permit a reliable health-economic evaluation.
Reference standard
For fracture-related diagnostic analyses, a radiologist experienced in musculoskeletal imaging and a senior orthopaedic trauma surgeon reviewed the index radiographs and available clinical information. Disagreements were resolved by consensus or, when necessary, by a third expert. The assessment distinguished fractures visible on the original radiographs from fractures detectable only on subsequent imaging.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Participants were individually randomized in a 1:1 ratio to AI-assisted radiograph interpretation or standard radiograph interpretation without AI assistance. A single global randomization sequence was generated by the trial statistician and used across all three participating centers.
| Arm | Type | Receives |
|---|---|---|
| Standard Radiograph Interpretation Without AI | No intervention | –Plain radiographs are interpreted by the frontline treating physician according to routine clinical practice without access to AI output. All diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. Formal radiology reports are generally available later and are not routinely available before completion of the emergency department encounter. |
| AI-Assisted Radiograph Interpretation | Experimental | Diagnostic Test: BoneView AI-Assisted Radiograph InterpretationPlain radiographs are interpreted by the frontline treating physician with access to real-time output from BoneView version 2.3.8 (Gleamer, Paris, France). The AI output is used as diagnostic decision support only; all final diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. |
Who may qualify
- Ages
- 2 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- Age 2 years or older.
- Presentation to the emergency department following trauma or relevant joint complaint requiring plain radiographic imaging.
- Injury involving a single anatomical region within the supported anatomical scope of the AI system.
- Written informed consent provided by the participant or legally authorized representative, with age-appropriate assent where applicable.
Key exclusion criteria (7 listed)
- Age younger than 2 years.
- Injuries involving multiple anatomical regions.
- Head or cervical spine injuries.
- Previous radiographic imaging or medical assessment for the same injury before the index presentation.
- Contraindication to X-ray imaging, including pregnancy.
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 |
|---|---|---|
| BoneView AI-Assisted Radiograph InterpretationBoneView version 2.3.8 (Gleamer, Paris, France) analyzes DICOM radiographs and provides real-time visual annotations and classifications for supported musculoskeletal abnormalities. Physicians in the intervention group could view fracture-related findings as well as other supported outputs, including dislocations, joint effusions, and focal bone lesions. At University Hospital Salzburg and Regional Hospital Hallein, BoneView was delivered through the Aidoc aiOS platform (version 3.24.0). At University Hospital Nuremberg, BoneView was integrated directly into the local imaging workflow. BoneView was used as a decision-support tool and did not replace physician interpretation or the subsequent formal radiology report. | Diagnostic test | AI-Assisted Radiograph Interpretation |
Primary outcome
Time from Emergency Department Triage to Discharge
Time frame: During the index emergency department encounter (day 1).
Time in minutes from formal emergency department triage to discharge during the index encounter, compared between the AI-assisted and control groups.
4 secondary outcomes
Missed fractures
Time frame: At the initial diagnostic assessment during the index emergency department encounter (day 1), with subsequent expert reference-standard adjudication.
Additional Diagnostic Imaging Requested
Time frame: During the index emergency department encounter (day 1), following initial radiographic assessment.
Physician Diagnostic Confidence
Time frame: Measured immediately after the diagnosis
Diagnostic Accuracy of Fracture/Dislocation/Effusion/Bone Lesion Detection
Time frame: At the time of initial diagnosis, within 2 hours of patient presentation to the orthopedic emergency unit
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 6 Nov 2026.
- Derived from this study: PubMed 40870969Breitwieser M, Zirknitzer S, Poslusny K, Freude T, Scholsching J, Bodenschatz K, Wagner A, Hergan K, Schaffert M, Metzger R, Marko P. AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model. Diagnostics (Basel). 2025 Aug 21;15(16):2117. doi: 10.3390/diagnostics15162117.
- Individual participant dataSharing planned: Yes. De-identified individual participant data underlying the results reported in the primary publication will be made available to qualified researchers upon reasonable written request. Shared data may include participant demographics, anatomical injury region, frontline physician diagnosis, AI findings, expert-adjudicated reference-standard findings, diagnostic confidence, additional imaging, and timing data used to derive the primary endpoint. Data will be shared only for scientifically justified research purposes following review and approval of the proposed project by the study investigators and execution of an appropriate data-sharing agreement. Data sharing will comply with applicable institutional requirements, the General Data Protection Regulation (GDPR), and relevant Austrian and German data-protection legislation.
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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.
3 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing BoneView AI-Assisted Radiograph Interpretation in people with Bone Fractures, run by Salzburger Landeskliniken at 3 sites in 2 countries.
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 4 inclusion and 7 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 6 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Austria, Germany. 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?
Time from Emergency Department Triage to Discharge (time frame: During the index emergency department encounter (day 1).). 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 NCT06754137, which also lists 1 linked publication.
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 6 Nov 2026.