AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Use of IA for proximal humeral fracture prognosis.
- Who it may be relevant to
- Registry conditions: Proximal Humeral Fracture. Basic parameters: 18 years — 90 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES
Overview
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.
Interventions
- Other Use of IA for proximal humeral fracture prognosis
None (prognosis study)
Primary outcome measures
- Constant-Murley Score [Time frame: 1 year]
Eligibility criteria
Inclusion criteria
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.
Exclusion criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06467006 · 2024/5001