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Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...

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Updated: Mar 12, 2026

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

The Emory Knee Radiograph (MRKR) Dataset.

Brandon J Price¹, Judy Gichoya², Mohammadreza Chavoshi²

¹Department of Radiology, University of Florida, 1600 SW Archer Rd., Gainesville, FL, 32608, USA. brandon.price@ufhealth.org.

Journal of Imaging Informatics in Medicine

|March 11, 2026

Summary

This summary is machine-generated.

The Emory Knee Radiograph (MRKR) dataset offers 503,261 knee X-rays from diverse patients, including rich clinical data. This resource aids researchers in developing equitable deep learning models for osteoarthritis and pain management.

Keywords:

Dataset Knee Musculoskeletal Pain Radiograph

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Orthopedics and Musculoskeletal Research

Background:

Large-scale datasets are crucial for developing robust deep learning models in medical imaging.
Existing knee radiograph datasets often lack comprehensive clinical information and demographic diversity.
The Emory Knee Radiograph (MRKR) dataset addresses these limitations by providing extensive data.

Purpose of the Study:

To introduce and describe the MRKR dataset, a large, demographically diverse collection of knee radiographs.
To highlight the unique features of the MRKR dataset, including clinical data and imaging metadata.
To facilitate research in osteoarthritis, pain management, and the development of equitable AI models.

Main Methods:

Compilation of 503,261 knee radiographs from 83,011 patients.
Inclusion of de-identified DICOM imaging data.
Integration of detailed clinical information: patient-reported pain scores, diagnostic codes, procedural codes, image laterality, view type, and arthroplasty status.

Main Results:

The MRKR dataset comprises 503,261 knee radiographs, with 40% from African American patients, ensuring demographic diversity.
The dataset includes comprehensive clinical data and imaging metadata not commonly found in public repositories.
Data are publicly available under a CC-BY-SA license, promoting open research.

Conclusions:

The MRKR dataset is a valuable, openly accessible resource for advancing research in knee osteoarthritis and pain.
Its rich data facilitates the development of equitable deep learning models for improved patient outcomes.
Researchers can leverage this dataset to enhance diagnostic accuracy and treatment strategies for musculoskeletal conditions.