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

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.

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Key information

Conditions

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

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University third hospital

Beijing, Please Select An Option Below, 100089, China

About this study

The study applies magnetic resonance and deep learning (DL) to the diagnosis of joint synovial lesions, aims to have a more comprehensive understanding of the pathophysiology of the occurrence and development of joint synovial lesions. As a non-invasive imaging method to assess the condition of the disease, DL methods excavates the deep features contained in the image, quantifies the joint synovial lesions, and then gives more information to the clinician in the diagnosis and differential diagnosis of the joint synovial lesions, provide important information for the planning of individualized treatment plans for patients with joint synovial diseases.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

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.

Exclusion criteria

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

Treatment and study plan

Synovitis diagnosis

Diagnostic Test

Primary outcomes

Patient's diagnosis

Time frame: 2019-2022

Type of synovitis disease in patients with a clear comprehensive diagnosis

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Registry information

Official study title

Using Magnetic Resonance Imaging and DL Methods to Explore the Diagnosis and Clinical Prognosis of Joint Synovitis.

Important dates

Study start: 2012
Primary completion: 2022
Study completion: 2022
First posted: Jul 7, 2021
Registry last updated: Nov 2, 2022

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.

Published trials that share one or more normalized conditions with this study.

Completed