Clinical Study of Magnetic Resonance Imaging and Deep Learning of Joint Synovial Disease

NCT04952896 | Completed

• 1 other identifier: LM2019168
• Study Type: observational
• Target: 350
• Locations: 1 country
• Sites: 1

Brief Summary

Through the high-throughput feature extraction of magnetic resonance images, the deep learning prediction model of joint synovial lesions is constructed used for the diagnosis, differential diagnosis and curative effect monitoring of joint synovial lesions.

Conditions

Synovial Diseases; Pigmented Villonodular Synovitis; Gout; Rheumatoid Arthritis

Keywords

pigmented villonodular synovitis; deep learning; knee synovitis; MRI

Outcome Measures

Primary Outcomes (1)

• Patient's diagnosis

  Type of synovitis disease in patients with a clear comprehensive diagnosis

  2019-2022

Study Arms (3)

Group of patients with pigmented villonodular synovitis

Diagnosis confirmed by arthroscopic pathological biopsy.

Diagnostic Test: Synovitis diagnosis

Group of patients with rheumatoid arthritis

Diagnosis determined by clinical history, laboratory tests and arthroscopic pathology biopsy.

Diagnostic Test: Synovitis diagnosis

Group of patients with gout

Diagnosis was determined by laboratory tests, energy spectrum imaging and arthroscopic pathology biopsy.

Diagnostic Test: Synovitis diagnosis

Interventions

Synovitis diagnosis DIAGNOSTIC_TEST

Group of patients with gout; Group of patients with pigmented villonodular synovitis; Group of patients with rheumatoid arthritis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with joint synovial disease through radiological examination, arthroscopy or pathological biopsy of the joint, or whose clinical manifestations meet the diagnostic criteria of the American College of Rheumatology (ACR) for joint synovial disease.

You may qualify if:

• Patients diagnosed with joint synovial disease through radiological examination, arthroscopy or pathological biopsy of the joint, or whose clinical manifestations meet the diagnostic criteria of the American College of Rheumatology (ACR) for joint synovial disease.
• Patients received pre-treatment MR.

You may not qualify if:

• Patients who have received surgery, medication or other systemic treatment before standardized MRI scan.
• Poor image quality.
• Articular hemorrhage.

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (1)

Peking University third hospital

Beijing, Please Select An Option Below, 100089, China

Location

MeSH Terms

Conditions

Synovitis, Pigmented Villonodular; Gout; Arthritis, Rheumatoid

Condition Hierarchy (Ancestors)

Giant Cell Tumor of Tendon Sheath; Giant Cell Tumors; Neoplasms, Connective Tissue; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Synovitis; Joint Diseases; Musculoskeletal Diseases; Tendinopathy; Muscular Diseases; Arthritis; Crystal Arthropathies; Rheumatic Diseases; Purine-Pyrimidine Metabolism, Inborn Errors; Metabolism, Inborn Errors; Genetic Diseases, Inborn; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Metabolic Diseases; Nutritional and Metabolic Diseases; Connective Tissue Diseases; Skin and Connective Tissue Diseases; Autoimmune Diseases; Immune System Diseases

Study Design

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

Study Record Dates

• First Submitted: June 29, 2021
• First Posted: July 7, 2021
• Study Start: January 1, 2012
• Primary Completion: March 31, 2022
• Study Completion: October 29, 2022
• Last Updated: November 2, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ANALYTIC CODE

Locations

CN (1)