FracturesNCT06644391
Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning
An observational study of Fractures and Musculoskeletal, run by Carebot s.r.o. at 1 site in Czechia.
Trial summary
This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays. By comparing the performance of a deep learning AI model with that of experienced radiologists, we seek to understand how AI can help improve fracture detection accuracy in… Read more about this study
- Who is it for?
- Ages 1 year and older
- Conditions the study is for
- Fractures, Musculoskeletal
- What is being tested?
- Carebot AI Bones
- How many people
- 600 participants
- Where
- 1 siteCzechia
- Study dates
- 20 Mar 2023to 15 Jul 2024
- 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 1 year and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study includes a retrospective cohort of pediatric and adult patients who underwent musculoskeletal radiographs between March 20 and May 8, 2023, in a single-center hospital setting.
Who may be able to join
- Patients aged 1 year or older.
- Musculoskeletal X-rays available in Digital Imaging and Communications in Medicine (DICOM) format.
- At least one digital plain radiograph of an appendicular body part, including the foot, ankle, knee, hand, wrist, elbow, shoulder, or pelvis.
Who cannot join
- Poor radiographic quality that precludes human interpretation.
- Radiographs of the lumbar, thoracic, and cervical spine, or facial/nasal bones.
- Radiographs that do not meet the inclusion criteria for appendicular body parts.
Trial locations and contacts
1 site in 1 country.
Czechia1 site
Nemocnice ve Frýdku-Místku, p.o.
Frýdek-Místek, Moravian-Silesian Region, 73801
About this study
Official title: A Retrospective Multi-reader Study of Diagnostic Performance: Carebot AI Bones 1.2 (Deep Learning Algorithms v1.0), Frýdek-Místek Hospital
This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays. By comparing the performance of a deep learning AI model with that of experienced radiologists, we seek to understand how AI can help improve fracture detection accuracy in clinical settings. The study analyzed 600 X-rays from both pediatric and adult patients, focusing on identifying fractures across different body parts, including the foot, ankle, knee, hand, wrist, and more. The findings show that integrating AI can increase radiologists' sensitivity in detecting fractures, potentially improving patient outcomes by reducing the number of missed injuries.
- Conditions
- Fractures, Musculoskeletal
- Being tested
- Carebot AI Bones
- Medical subject headings
- Fractures, Bone
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Carebot AI BonesDiagnostic test
The use of a deep learning-based artificial intelligence software, Carebot AI Bones version 1.2.2, designed to aid in the detection of fractures on musculoskeletal radiographs. The AI model analyzes digital X-ray images to identify fractures, highlighting areas of interest with bounding boxes.
| Study group | Role | What they receive |
|---|---|---|
| Radiographs Analyzed Using AI and Radiologist Review | This cohort consists of 600 radiographs collected from pediatric and adult patients, aged 1 to 99 years, who underwent X-ray imaging for musculoskeletal conditions. The radiographs include various body parts such as the foot, ankle, knee, hand, wrist, elbow, shoulder, and pelvis. Fractures were present in 95 cases, while 453 cases showed no fractures. Receives: Diagnostic Test: Carebot AI Bones |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays
This outcome measures the sensitivity of the AI model (Carebot AI Bones 1.2.2) in detecting fractures on musculoskeletal X-rays, compared to the sensitivity of radiologists with varying levels of experience. Sensitivity is calculated as the proportion of true positive fracture cases identified by the AI model and radiologists out of all confirmed fracture cases.
Measured: From March 2023 to May 2023 (Retrospective analysis period)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2024
- Study start20 Mar 2023
- Primary completion15 Jul 2024
- Study completion15 Jul 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 600 participants (actual)
Sponsor and investigators
- Lead sponsor
- Carebot s.r.o.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06644391
- Sponsor's protocol ID
- CB-BONES-01-FM
- Status last verified
- Mar 2026
- First submitted
- 14 Oct 2024
- Last updated
- 18 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to privacy concerns and the retrospective nature of the study, individual participant data (IPD) will not be shared. Data collected contains sensitive medical information that is protected under confidentiality agreements and GDPR regulations.
Study at a glance
From the registry record, last updated by the sponsor on 18 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Retrospective Multi-reader Study of Diagnostic Performance: Carebot AI Bones 1.2 (Deep Learning Algorithms v1.0), Frýdek-Místek Hospital
- ClinicalTrials.gov ID
- NCT06644391
- Acronym / sponsor ID
- CB-BONES-01-FM
- Conditions
- Fractures, Musculoskeletal
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 600 participants (actual)
- Lead sponsor
- Carebot s.r.o.Industry (pharma, biotech, devices)
- Study start
- 20 Mar 2023 (actual)
- Primary completion
- 15 Jul 2024 (actual)
- Study completion
- 15 Jul 2024 (actual)
- First posted
- 16 Oct 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 retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays. By comparing the performance of a deep learning AI model with that of experienced radiologists, we seek to understand how AI can help improve fracture detection accuracy in clinical settings. The study analyzed 600 X-rays from both pediatric and adult patients, focusing on identifying fractures across different body parts, including the foot, ankle, knee, hand, wrist, and more. The findings show that integrating AI can increase radiologists' sensitivity in detecting fractures, potentially improving patient outcomes by reducing the number of missed injuries.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Radiographs Analyzed Using AI and Radiologist Review | – | Diagnostic Test: Carebot AI BonesThis cohort consists of 600 radiographs collected from pediatric and adult patients, aged 1 to 99 years, who underwent X-ray imaging for musculoskeletal conditions. The radiographs include various body parts such as the foot, ankle, knee, hand, wrist, elbow, shoulder, and pelvis. Fractures were present in 95 cases, while 453 cases showed no fractures. |
Who may qualify
- Ages
- 1 year and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study includes a retrospective cohort of pediatric and adult patients who underwent musculoskeletal radiographs between March 20 and May 8, 2023, in a single-center hospital setting.
Key inclusion criteria (3 listed)
- Patients aged 1 year or older.
- Musculoskeletal X-rays available in Digital Imaging and Communications in Medicine (DICOM) format.
- At least one digital plain radiograph of an appendicular body part, including the foot, ankle, knee, hand, wrist, elbow, shoulder, or pelvis.
Key exclusion criteria (3 listed)
- Poor radiographic quality that precludes human interpretation.
- Radiographs of the lumbar, thoracic, and cervical spine, or facial/nasal bones.
- Radiographs that do not meet the inclusion criteria for appendicular body parts.
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 |
|---|---|---|
| Carebot AI BonesThe use of a deep learning-based artificial intelligence software, Carebot AI Bones version 1.2.2, designed to aid in the detection of fractures on musculoskeletal radiographs. The AI model analyzes digital X-ray images to identify fractures, highlighting areas of interest with bounding boxes. | Diagnostic test | Radiographs Analyzed Using AI and Radiologist Review |
Primary outcome
Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays
Time frame: From March 2023 to May 2023 (Retrospective analysis period)
This outcome measures the sensitivity of the AI model (Carebot AI Bones 1.2.2) in detecting fractures on musculoskeletal X-rays, compared to the sensitivity of radiologists with varying levels of experience. Sensitivity is calculated as the proportion of true positive fracture cases identified by the AI model and radiologists out of all confirmed fracture cases.
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 18 Mar 2026.
- Individual participant dataSharing planned: No. Due to privacy concerns and the retrospective nature of the study, individual participant data (IPD) will not be shared. Data collected contains sensitive medical information that is protected under confidentiality agreements and GDPR regulations.
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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 Fractures and Musculoskeletal, run by Carebot s.r.o. at 1 site in Czechia.
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 3 inclusion and 3 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 18 Mar 2026. 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 Czechia. 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?
Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays (time frame: From March 2023 to May 2023 (Retrospective analysis period)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 18 Mar 2026.