医学
DICOM
骨关节炎
射线照相术
医学物理学
人工智能
膝关节痛
元数据
放射科
医学影像学
深度学习
数据收集
计算机科学
梅德林
预测值
患者数据
计算机断层摄影术
物理疗法
数字射线照相术
价值(数学)
作者
Brandon Price,Judy Wawira Gichoya,Mohammadreza Chavoshi,Jason Adleberg,Kaesha Thomas,Zachary Zaiman,Aawez Mansuri,Beatrice Brown-Mulry,Chima Okechukwu,Theo Dapamede,Frank Li,Rohan Isaac,Oren Goltzer,Michael Brandon Gottschalk,Hari Trivedi
标识
DOI:10.1007/s10278-026-01902-6
摘要
The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset provides imaging data in DICOM format along with detailed clinical information, including patient-reported pain scores, diagnostic codes, and procedural codes, which are not commonly available in similar datasets. The MRKR dataset also features imaging metadata such as image laterality, view type, and presence of arthroplasty, enhancing its value for research and model development. Data are de-identified and available publicly and openly under a CC-BY-SA license. This dataset provides an opportunity for researchers studying osteoarthritis and pain to develop equitable deep learning models to help improve patient outcomes and pain.
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