Development of web-based management system and dataset for radiology-common data model (R-CDM) and its clinical application in liver cirrhosis

Abstract

The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) used in distributed research networks has low coverage of clinical data and does not reflect the latest trends of precision medicine. Radiology data have great merits to visualize and identify the lesions in specific diseases. However, radiology data should be shared to obtain the sufficient scale and diversity required to provide strong evidence for improving patient care. Our study was to develop a web-based management system for radiology-CDM (R-CDM), as an extension of the OMOP-CDM, and to assess the feasibility of an R-CDM dataset for application of radiological image data in AI learning. This study standardized a cirrhosis of liver (LC) R-CDM dataset consisting of CT data (LC 40,575 images vs. non-LC 33,565 images). With use of modified AI learning algorithm, the diagnostic accuracy was 0.99292 (error rate = 0.00708), and its sensitivity was 0.99469 for LC and specificity was 0.99115 for non-LC. We developed a web-based management system for searching and downloading standardized R-CDM dataset and constructed a liver cirrhosis R-CDM dataset for clinical practice. Our management system and LC dataset would be helpful for multicenter study and AI learning research.