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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04952896
Other study ID # LM2019168
Secondary ID
Status Completed
Phase
First received
Last updated
Start date January 1, 2012
Est. completion date October 29, 2022

Study information

Verified date May 2022
Source Peking University Third Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Description:

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.


Recruitment information / eligibility

Status Completed
Enrollment 350
Est. completion date October 29, 2022
Est. primary completion date March 31, 2022
Accepts healthy volunteers No
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: 1. 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. 2. Patients received pre-treatment MR. Exclusion Criteria: 1. Patients who have received surgery, medication or other systemic treatment before standardized MRI scan. 2. Poor image quality. 3. Articular hemorrhage.

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Synovitis diagnosis


Locations

Country Name City State
China Peking University third hospital Beijing Please Select An Option Below

Sponsors (1)

Lead Sponsor Collaborator
Peking University Third Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Patient's diagnosis Type of synovitis disease in patients with a clear comprehensive diagnosis 2019-2022
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