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Multi-Institutional Collaborative Research Using Ophthalmic Medical Image Data Standardized by Radiology Common Data
ChulHyoung Park1, Sang Jun Park2, Da Yun Lee2
1Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.
Studies in Health Technology and Informatics
|January 25, 2024
Summary
A new Radiology Common Data Model (R-CDM) standardizes medical imaging data, enabling efficient multi-institutional research. Analysis revealed hypertension is linked to thinner retinal layers using this novel data model.
Area of Science:
- Ophthalmology
- Medical Informatics
- Health Data Standardization
Background:
- The Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) standardizes electronic health records but excludes unstructured medical image data.
- This limitation hinders multi-institutional collaborative research involving medical imaging.
- Standardizing medical imaging data is crucial for advancing large-scale clinical studies.
Purpose of the Study:
- To develop and validate the Radiology Common Data Model (R-CDM) for standardizing medical imaging data.
- To demonstrate the utility of R-CDM in analyzing the relationship between chronic diseases and retinal structure.
- To facilitate efficient multi-institutional collaborative research by integrating imaging and clinical data.
Main Methods:
- Development of the Radiology Common Data Model (R-CDM) to standardize medical imaging data.
- Standardization of 737,500 Optical Coherence Tomography (OCT) datasets from two South Korean tertiary hospitals using R-CDM.
- Analysis of the association between hypertension and retinal thickness metrics (central macular thickness, RNFL thickness) within the R-CDM framework.
Main Results:
- Successfully standardized a large cohort of Optical Coherence Tomography (OCT) data using the R-CDM.
- Identified statistically significant reductions in central macular thickness and retinal nerve fiber layer (RNFL) thickness in patients with hypertension compared to controls.
- Demonstrated the feasibility of efficient multi-institutional research by simultaneously analyzing medical imaging and clinical data.
Conclusions:
- The Radiology Common Data Model (R-CDM) effectively standardizes medical imaging data, overcoming limitations of existing models.
- R-CDM enables efficient, simultaneous analysis of medical imaging and clinical data for multi-institutional research.
- The study highlights a significant association between hypertension and reduced retinal thickness, validated through the R-CDM framework.
