Emory Knee Radiograph (MRKR) Dataset
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909375694635008 |
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| author | Price, Brandon Adleberg, Jason Thomas, Kaesha Zaiman, Zach Mansuri, Aawez Brown-Mulry, Beatrice Okecheukwu, Chima Gichoya, Judy Trivedi, Hari |
| author_facet | Price, Brandon Adleberg, Jason Thomas, Kaesha Zaiman, Zach Mansuri, Aawez Brown-Mulry, Beatrice Okecheukwu, Chima Gichoya, Judy Trivedi, Hari |
| contents | 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 hardware, enhancing its value for research and model development. MRKR addresses significant gaps in existing datasets by offering a more representative sample for studying osteoarthritis and related outcomes, particularly among minority populations, thereby providing a valuable resource for clinicians and researchers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emory Knee Radiograph (MRKR) Dataset Price, Brandon Adleberg, Jason Thomas, Kaesha Zaiman, Zach Mansuri, Aawez Brown-Mulry, Beatrice Okecheukwu, Chima Gichoya, Judy Trivedi, Hari Computer Vision and Pattern Recognition Artificial Intelligence 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 hardware, enhancing its value for research and model development. MRKR addresses significant gaps in existing datasets by offering a more representative sample for studying osteoarthritis and related outcomes, particularly among minority populations, thereby providing a valuable resource for clinicians and researchers. |
| title | Emory Knee Radiograph (MRKR) Dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.00866 |