AI PREDICTION FOR PROXIMAL HUMERAL FRACTURES (Orthopredict)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Proximal Humeral Fracture
• Intervention / Treatment: Other: Use of IA for proximal humeral fracture prognosis

• Study Type: Observational
• Enrollment (Estimated): 500

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

Patients sustaining proximal humeral fractures, nonoperatively treated

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Constant-Murley Score	Functional outcome	1 year

Collaborators and Investigators

• Sponsor: Consorci Sanitari de l'Anoia
• Collaborators: Parc de Salut Mar; Parc Taulí Hospital Universitari

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2024
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): September 1, 2027

Study Registration Dates

• First Submitted: June 14, 2024
• First Submitted That Met QC Criteria: June 14, 2024
• First Posted (Estimated): June 20, 2024

Study Record Updates

• Last Update Posted (Estimated): June 20, 2024
• Last Update Submitted That Met QC Criteria: June 14, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Wounds and Injuries; Shoulder Injuries; Arm Injuries; Fractures, Bone; Humeral Fractures; Shoulder Fractures
• Other Study ID Numbers: 2024/5001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No