Yonsei Med J.
2022 Jan;63(S1):74-83. 10.3349/ymj.2022.63.S74.
Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data
- Affiliations
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- 1Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- 2Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
- 3Medical Convergence Research Center, Wonkwang University, Iksan, Korea
- 4Department of Radiology, Ajou University Medical Center, Suwon, Korea
- 5Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
- KMID: 2524806
- DOI: http://doi.org/10.3349/ymj.2022.63.S74
Abstract
- Purpose
Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM).
Materials and Methods
R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database.
Results
Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and RCDM were linked to efficiently extract specific types of images from specific patient cohorts.
Conclusion
R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.
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