VISION: Toward a Standardized Process for Radiology Image Management at the National Level
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909184179568640 |
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| author | Knight, Kathryn Danciu, Ioana Ovchinnikova, Olga Hinkle, Jacob Shekar, Mayanka Chandra Mukherjee, Debangshu McAllister, Eileen Rizy, Caitlin Cho, Kelly Justice, Amy C. Erdos, Joseph Kuzmak, Peter Costa, Lauren Ho, Yuk-Lam Madipadga, Reddy Tamang, Suzanne Goethert, Ian |
| author_facet | Knight, Kathryn Danciu, Ioana Ovchinnikova, Olga Hinkle, Jacob Shekar, Mayanka Chandra Mukherjee, Debangshu McAllister, Eileen Rizy, Caitlin Cho, Kelly Justice, Amy C. Erdos, Joseph Kuzmak, Peter Costa, Lauren Ho, Yuk-Lam Madipadga, Reddy Tamang, Suzanne Goethert, Ian |
| contents | The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotation available for model training, modality, and patient identifiers. In this paper, we describe our experiences and challenges in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. We also discuss implications in making this repository research-ready for medical investigators. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment, as well as roadblocks and bottlenecks in this process that may hinder future efforts at automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18842 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VISION: Toward a Standardized Process for Radiology Image Management at the National Level Knight, Kathryn Danciu, Ioana Ovchinnikova, Olga Hinkle, Jacob Shekar, Mayanka Chandra Mukherjee, Debangshu McAllister, Eileen Rizy, Caitlin Cho, Kelly Justice, Amy C. Erdos, Joseph Kuzmak, Peter Costa, Lauren Ho, Yuk-Lam Madipadga, Reddy Tamang, Suzanne Goethert, Ian Computer Vision and Pattern Recognition The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotation available for model training, modality, and patient identifiers. In this paper, we describe our experiences and challenges in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. We also discuss implications in making this repository research-ready for medical investigators. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment, as well as roadblocks and bottlenecks in this process that may hinder future efforts at automation. |
| title | VISION: Toward a Standardized Process for Radiology Image Management at the National Level |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.18842 |