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AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES

NCT06467006 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2024-06-20

No results posted yet for this study

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.

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

Interventions

OTHER

Use of IA for proximal humeral fracture prognosis

None (prognosis study)

Sponsors & Collaborators

  • Parc de Salut Mar

    collaborator OTHER

  • Parc Taulí Hospital Universitari

    collaborator OTHER

  • Consorci Sanitari de l'Anoia

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-09-30
Primary Completion
2026-12-31
Completion
2027-09-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06467006 on ClinicalTrials.gov