Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data

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 R-CDM 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.

Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

“…The working group evaluated standard vocabularies, defined fields containing key imaging events, and identified limitations of the model. The working group started with the R-CDM in the development of the medical imaging extension [ 6 ]. Imaging researchers across the field were consulted to gather requirements and gain insights into the structure and usability of the proposed model.…”

“…Park et al (2022) developed the first OMOP imaging extension for radiological imaging studies (R-CDM) [ 6 ]. While the R-CDM adeptly bridges structured data from OMOP CDM to DICOM headers, it does not encompass feature information pertinent to medical imaging and remains confined to radiological data.…”

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

“…By integrating data from multiple sources into a standardized format, researchers can build and test models that can be used to identify patients who are at a higher risk for cancer or who may benefit from personalized treatment plans. Moreover, the Radiology Common Data Model (R-CDM) for the standardization of Digital Imaging Communications in Medicine (DICOM) was published in 2022 [ 13 ]. The R-CDM contains 75,000 radiology terms to harmonize DICOM imaging data into two extended tables, radiology occurrence and radiology image, on the OMOP CDM.…”

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

“…Different types of medical imaging are used to scan the human body, such as Magnetic resonance imaging, X-ray, Ultrasound imaging, Computed tomography Scanning, etc. Nowadays, the development and need for medical imaging modality have tremendously increased, and the need for producing, transferring, and sharing medical images has also been amplified 3 , 4 .…”