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Completed

NCT Number: NCT07784686

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

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.

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Key information

Conditions

Knee Joint

Sex eligibility

All sexes

Study type

Observational

Primary location

Tangdu Hospital, Fourth Military Medical University

Xi'an, Shaanxi, 710038, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

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

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.

Treatment and study plan

Primary outcomes

  1. Dice Similarity Coefficient for AI-Based Knee MRI Segmentation

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Tang-Du Hospital

Other

Registry information

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Aug 25, 2026
Registry last updated
Aug 25, 2026

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.

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