Proximal Humeral FractureOrthopredictNCT06467006
AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES
An observational study of Proximal Humeral Fracture, run by Consorci Sanitari de l'Anoia.
Trial summary
Our smartphones can recognize the pictures of our family, loved ones and friends. Face recognition software leverages artificial intelligence (AI), image recognition and other advanced technology to map, analyze and confirm the identity of a face. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Proximal Humeral Fracture
- What is being tested?
- Use of IA for proximal humeral fracture prognosis
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- Sep 2024to Sep 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 to 90 years
- All sexes
- Adult, Older adult
Study population: Patients sustaining proximal humeral fractures, nonoperatively treated
Who may be able to join
- Patients sustaining a proximal humerus fracture treated nonoperatively under the criteria of the treating surgeon and patients' preference.
- Subjects evaluated within the first 3 weeks after the injury. Patients between 18 and 90 years of age. Patients who have been studied with simple shoulder radiographs in anteroposterior and scapular outlet projections.
- Participants who accept 1-year time follow-up.
Who cannot join
- Patients with dementia or difficulty completing the evaluation after one year of follow-up.
- Patients who have previously received surgical treatment on the affected limb. Patients who have suffered a previous fracture in the affected limb. Surgically treated patients.
About this study
Official title: ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES
Our smartphones can recognize the pictures of our family, loved ones and friends. Face recognition software leverages artificial intelligence (AI), image recognition and other advanced technology to map, analyze and confirm the identity of a face.
We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture. In consequence, there is great heterogeneity in the treatment of proximal humerus fractures. Moreover, offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging, given that the current series are based on obsolete classifications, and the published series bring together just over hundreds of patients analyzed. With these limitations, patients have few opportunities to participate in decision-making about their injury.
The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture. In brief, AI can help identify similar fracture patterns without human inference, while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture.
- Conditions
- Proximal Humeral Fracture
- Being tested
- Use of IA for proximal humeral fracture prognosis
- Medical subject headings
- Shoulder Fractures
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Use of IA for proximal humeral fracture prognosisOther
None (prognosis study)
What is being measured
The primary outcome is the main question the study is designed to answer.
Constant-Murley Score
Functional outcome
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jun 2024
- Study startSep 2024 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionSep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Proximal Humeral 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
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Consorci Sanitari de l'Anoia
- Sponsor type
- University, hospital or other
- Collaborators
- Parc de Salut Mar, Parc Taulí Hospital Universitari
- Responsible party
- Principal investigator, Joan Miquel, Parc Taulí Hospital Universitari
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06467006
- Sponsor's protocol ID
- 2024/5001
- Acronym
- Orthopredict
- Status last verified
- Jun 2024
- First submitted
- 14 Jun 2024
- Last updated
- 20 Jun 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES
- ClinicalTrials.gov ID
- NCT06467006
- Acronym / sponsor ID
- Orthopredict / 2024/5001
- Conditions
- Proximal Humeral Fracture
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Consorci Sanitari de l'AnoiaUniversity, hospital or other
- Collaborators
- Parc de Salut Mar, Parc Taulí Hospital Universitari
- Study start
- Sep 2024 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Sep 2027 (estimated)
- First posted
- 20 Jun 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.
Our smartphones can recognize the pictures of our family, loved ones and friends. Face recognition software leverages artificial intelligence (AI), image recognition and other advanced technology to map, analyze and confirm the identity of a face.
We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture. In consequence, there is great heterogeneity in the treatment of proximal humerus fractures. Moreover, offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging, given that the current series are based on obsolete classifications, and the published series bring together just over hundreds of patients analyzed. With these limitations, patients have few opportunities to participate in decision-making about their injury.
The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture. In brief, AI can help identify similar fracture patterns without human inference, while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients sustaining proximal humeral fractures, nonoperatively treated
Key inclusion criteria (3 listed)
- Patients sustaining a proximal humerus fracture treated nonoperatively under the criteria of the treating surgeon and patients' preference.
- Subjects evaluated within the first 3 weeks after the injury. Patients between 18 and 90 years of age. Patients who have been studied with simple shoulder radiographs in anteroposterior and scapular outlet projections.
- Participants who accept 1-year time follow-up.
Key exclusion criteria (2 listed)
- Patients with dementia or difficulty completing the evaluation after one year of follow-up.
- Patients who have previously received surgical treatment on the affected limb. Patients who have suffered a previous fracture in the affected limb. Surgically treated patients.
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 |
|---|---|---|
| Use of IA for proximal humeral fracture prognosisNone (prognosis study) | Other | – |
Primary outcome
Constant-Murley Score
Time frame: 1 year
Functional outcome
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jun 2024.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Proximal Humeral Fracture, run by Consorci Sanitari de l'Anoia.
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 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: opening soon, as of the sponsor's last update on 20 Jun 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
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?
Constant-Murley Score (time frame: 1 year). 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 20 Jun 2024.