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NCT Number: NCT06467006

AI PREDICTION FOR 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.

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Key information

Conditions

Proximal Humeral Fracture Fractures, Bone Shoulder Fractures Shoulder Injuries Wounds and Injuries

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Use of IA for proximal humeral fracture prognosis

Other

None (prognosis study)

Primary outcomes

  1. Constant-Murley Score

    Time frame: 1 year

    Functional outcome

Sponsors and collaborators

Lead sponsor

Consorci Sanitari de l'Anoia

Other

Collaborators

  • Parc Taulí Hospital Universitari
  • Parc de Salut Mar

Registry information

Official study title

ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES

Acronym: Orthopredict

Important dates

Study start
2024
Primary completion
2026
Study completion
2027
First posted
Jun 20, 2024
Registry last updated
Jun 20, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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