SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis

NCT04953026 | Unknown

• 1 other identifier: M2020458
• Study Type: observational
• Target: 800
• Locations: 1 country
• Sites: 1

Brief Summary

This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.

Conditions

Superior Labrum From Anterior to Posterior Injuries

Keywords

artificial intelligence; SLAP; diagnosis

Outcome Measures

Primary Outcomes (1)

• SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis

  The model of deep learning was obtained for diagnosis and grading of SLAP injury and compared with the radiologists of different stages.

  2021.10.1-2022.7.1

Study Arms (3)

Normal control group-Grade 0

Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.

Diagnostic Test: Diagnositic test

Ligament injury -Grade 1

Arthroscopic examination of the shoulder showed labrum degeneration or injury, but no local or complete tear.

Diagnostic Test: Diagnositic test

Ligament tear-Grade 2

Arthroscopy of the shoulder revealed partial or complete loss of labrum.

Diagnostic Test: Diagnositic test

Interventions

Diagnositic test DIAGNOSTIC_TEST

The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.

Ligament injury -Grade 1; Ligament tear-Grade 2; Normal control group-Grade 0

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.

You may qualify if:

• Without any treatment before imaging examination;
• MR of the shoulder joint was performed within 3 months before the operation and the image quality was good;
• Arthroscopic operation was performed in our hospital, and the operation records were complete.

You may not qualify if:

• History of shoulder surgery, tumor, or previous fracture;
• Unclear image, serious artifact, or incomplete clinical data.

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (1)

Peking University Third Hospital

Beijing, Beijing Municipality, 010, China

Location

MeSH Terms

Conditions

Shoulder Injuries; Disease

Condition Hierarchy (Ancestors)

Wounds and Injuries; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

huishu Yuan, MD

CONTACT

15810245738; huishuy@bjmu.edu.cn

Ming Ni, MD

CONTACT

+8613884794867; sdyingxiang2017@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 29, 2021
• First Posted: July 7, 2021
• Study Start: October 1, 2021
• Primary Completion: June 1, 2022
• Study Completion: July 1, 2022
• Last Updated: July 7, 2021

Record last verified: 2021-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)