Multi-Institutional Collaborative Research Using Ophthalmic Medical Image Data Standardized by Radiology Common Data Model (R-CDM)

被引：0
|
作者：
Park, ChulHyoung [1 ]
Park, Sang Jun [2 ]
Lee, Da Yun [2 ]
You, Seng Chan [3 ]
Lee, Kihwang [4 ]
Park, Woong [1 ,5 ]
机构：
[1] Ajou Univ, Dept Biomed Informat, Sch Med, 164 Worldcup Ro, Suwon 16499, South Korea
[2] Seoul Natl Univ, Dept Ophthalmol, Bundang Hosp, Seongnam, South Korea
[3] Yonsei Univ, Dept Biomed Syst Informat, Coll Med, Seoul, South Korea
[4] Ajou Univ, Dept Ophthalmol, Sch Med, Suwon, South Korea
[5] Ajou Univ, Dept Biomed Sci, Grad Sch Med, Suwon, South Korea
来源：
MEDINFO 2023 - THE FUTURE IS ACCESSIBLE | 2024年 / 310卷
关键词：
Medical imaging data; data standardization; ophthalmology;
D O I：
10.3233/SHTI230925
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) is an international standard model for standardizing electronic medical record data. However, unstructured data such as medical image data which is beyond the scope of standardization by the current OMOP-CDM is difficult to be used in multi-institutional collaborative research. Therefore, we developed the Radiology-CDM (R-CDM) which standardizes medical imaging data. As a proof of concept, 737,500 Optical Coherence Tomography (OCT) data from two tertiary hospitals in South Korea is standardized in the form of R-CDM. The relationship between chronic disease and retinal thickness was analyzed by using the R-CDM. Central macular thickness and retinal nerve fiber layer (RNFL) thickness were significantly thinner in the patients with hypertension compared to the control cohort. It is meaningful in that multi-institutional collaborative research using medical image data and clinical data simultaneously can be conducted very efficiently.
引用
收藏
页码：48 / 52
页数：5
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