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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07784686

Enrollment

9500

Registered

2026-08-25

Start date

2026-07-02

Completion date

2026-08-01

Last updated

2026-08-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Knee Joint

Keywords

Knee Joint

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Yes

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 outcomes

Primary

Measure	Time frame	Description
Dice Similarity Coefficient for AI-Based Knee MRI Segmentation	At completion of retrospective MRI image analysis	The Dice similarity coefficient will be used to evaluate the spatial agreement between artificial intelligence-generated segmentations and reference annotations. The Dice coefficient ranges from 0 to 1, with higher values indicating greater agreement.

Countries

China

Outcome results