Lee Chung-Sub, Kim Ji-Eon, No Si-Hyeong, Kim Tae-Hoon, Yoon Kwon-Ha, Jeong Chang-Won

KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 10, pp. 239-246, Oct. 2020
https://doi.org/10.3745/KTCCS.2020.9.10.239,   PDF Download:
Keywords: DICOM, Radiology_CDM, Medical Bigdata, Artificial Intelligence Training Platform, Machine Learning
Abstract

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

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