VISION: Toward a Standardized Process for Radiology Image Management at the National Level
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.
Authors
- Yuk-Lam Ho
- Ioana Danciu (ORCID: https://orcid.org/0000-0002-0164-1403)
- Ian Goethert (ORCID: https://orcid.org/0000-0002-6978-1939)
- Amy C. Justice (ORCID: https://orcid.org/0000-0003-0139-5502)
- Lauren Costa (ORCID: https://orcid.org/0000-0001-7237-5336)
- Suzanne Tamang (ORCID: https://orcid.org/0000-0003-2077-4620)
- Jacob Hinkle (ORCID: https://orcid.org/0000-0002-7751-1760)
- Debangshu Mukherjee (ORCID: https://orcid.org/0000-0003-0437-9807)
- Kelly Cho (ORCID: https://orcid.org/0000-0003-1727-7076)
- Joseph Erdos (ORCID: https://orcid.org/0000-0003-4442-0683)
- Kathryn Knight (ORCID: https://orcid.org/0000-0001-5190-3152)
- Olga S. Ovchinnikova (ORCID: https://orcid.org/0000-0001-8935-2309)
- Mayanka Chandrashekar (ORCID: https://orcid.org/0000-0002-3697-5972)
- Peter M. Kuzmak (ORCID: https://orcid.org/0000-0003-3511-0544)
- Caitlin Rizy
- Reddy Madipadga
- Eileen McAllister
Publication Details
- Journal
- International Journal of Media and Networks
- Published
- 2024-08-31
- DOI
- https://doi.org/10.33140/ijmn.02.08.05
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- U.S. Department of Energy
- Battelle
- Office of Research and Development
- UT-Battelle
- Oak Ridge National Laboratory