Feature Analysis of Knee Joint Magnetic Resonance Imaging Based on Artificial Intelligence | NCT07784686 | Trialant

CompletedObservationalUpdated Aug 25, 2026

Feature Analysis of Knee Joint Magnetic Resonance Imaging Based on Artificial Intelligence

Overview

Key facts that identify this study and show where it stands today.

ClinicalTrials.gov ID: NCT07784686
Lead Sponsor: Tang-Du HospitalOTHER
Overall Status: Completed
Study Type: Observational
Enrollment: 9,500Actual
Last Update Posted: Aug 25, 2026Actual
Start Date: Jul 2, 2026Actual
Primary Completion Date: Jul 20, 2026Actual
Completion Date: Aug 1, 2026Actual

Description

What the study is about and why it is being run, in the researchers' own words.

Official Title: Feature Analysis of Knee Joint Magnetic Resonance Imaging Based on Artificial Intelligence
Brief Summary: This retrospective observational study aims to investigate imaging features of the knee joint on magnetic resonance imaging (MRI) using artificial intelligence (AI)-based image analysis. Existing knee MRI examinations from eligible participants are retrospectively reviewed and analyzed. AI methods are used to identify and characterize anatomical structures and imaging abnormalities of the knee and to quantitatively evaluate relevant imaging features. The study aims to assess the feasibility and performance of AI-assisted MRI analysis and to explore its potential value in improving the objective and reproducible evaluation of knee joint imaging.
Detailed Description: This is a retrospective observational study based on previously acquired knee magnetic resonance imaging (MRI) data. Participants who underwent knee MRI examinations and met the predefined eligibility criteria are retrospectively included.

MRI images are analyzed using artificial intelligence-based image processing and analysis methods. The study focuses on the identification, segmentation, characterization, and quantitative assessment of knee joint structures and imaging abnormalities. Where applicable, AI-generated results are compared with reference assessments to evaluate model performance and the consistency of imaging measurements.

The study is intended to investigate the imaging characteristics of the knee joint, evaluate the performance and robustness of artificial intelligence-based MRI analysis methods, and explore their potential application in quantitative imaging assessment and computer-assisted evaluation of knee disorders.

Conditions

The medical conditions and diseases this study investigates.

Knee Joint

Interventions

The treatments and procedures being evaluated in this study.

No interventions

There are no interventions for this study.

Participation

Who can join this study and the criteria used to decide.

Ages Eligible for Study: Child (0-17)Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers: Yes
Eligibility Criteria: Inclusion Criteria:

Complete knee MRI data, including sagittal and coronal images. Adequate image quality without significant motion artifacts, metal artifacts, or magnetic susceptibility artifacts, with clear visualization of key anatomical structures of the knee.

Complete demographic and clinical information available for study grouping and analysis.

Exclusion Criteria:

Incomplete MRI data or poor image quality. History of knee surgery or implantation. Incomplete clinical information.

Design

How the study is designed and how its participant groups are organized.

Observational Model: Cohort
Time Perspective: Retrospective

Contacts

The people to reach with questions about this study or how to join it.

No central contacts

There are no central contacts for this study.

Locations

Where the study is run and whether each site is currently recruiting.

Tangdu Hospital, Fourth Military Medical University

Xi'an, Shaanxi 710038, China

Collaborators

The organizations and people involved in this study.

Lead Sponsor: Tang-Du HospitalOTHER

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