AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES

Launched by CONSORCI SANITARI DE L'ANOIA · Jun 14, 2024

Keywords

ClinConnect Summary

This clinical trial is studying how artificial intelligence (AI) can help improve the understanding and treatment of proximal humeral fractures, which are breaks near the top of the arm bone. Currently, doctors sometimes struggle to classify these injuries accurately, leading to different treatment approaches for patients. The goal of this trial is to use advanced technology to better predict how these fractures will heal and what complications might arise, helping patients and doctors make more informed decisions about treatment.

To participate in this trial, you need to be between 18 and 90 years old and have sustained a proximal humeral fracture that is being treated without surgery. You should be evaluated within three weeks after your injury and agree to follow-up for one year. However, if you have had previous surgeries on the same arm, other fractures in the same area, or have difficulty understanding the evaluation process, you may not be eligible. If you join the study, you can expect to share information about your injury, treatment, and recovery, which will help researchers improve future care for others with similar injuries.

Gender

ALL

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.