Browse > Article
http://dx.doi.org/10.3745/KTCCS.2020.9.10.239

Construction of Artificial Intelligence Training Platform for Multi-Center Clinical Research  

Lee, Chung-Sub (원광대학교 의료융합연구센터)
Kim, Ji-Eon (원광대학교 의료융합연구센터)
No, Si-Hyeong (원광대학교 의료융합연구센터)
Kim, Tae-Hoon (원광대학교 의료융합연구센터)
Yoon, Kwon-Ha (원광대학교 병원)
Jeong, Chang-Won (원광대학교 의료융합연구센터)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.9, no.10, 2020 , pp. 239-246 More about this Journal
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.
Keywords
DICOM; Radiology_CDM; Medical Bigdata; Artificial Intelligence Training Platform; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 G. Hripcsak and J. D. Duke, "Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers," in Stud Health Technology Information, Vol.216, pp.574-578. 2015.
2 F. FitzHenry, F. S. Resnic, S. L. Robbins, J. Denton, L. Nookala, D. Meeker, L. Ohno-Machado, and M. E. Matheny, "Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership," Applied Clinical Informatics, Vol.6, No.2, pp.536-547, 2015.   DOI
3 E. A Voss, R. Makadia, A. Matcho, Q. Ma, C. Knoll, M. Schuemie, F. J. DeFalco, A. Londhe, V. Zhu, and P. B. Ryan, "Feasibility and Utility of Applications of the Common Data Model to Multiple, Disparate Observational Health Databases," Journal of the American Medical Informatics Association, Vol.22, No.3, pp.553-564, 2015.   DOI
4 FeederNet [Internet], https://feedernet.com/
5 Radiology-CDM [Internet], https://github.com/WKUH-MCRC/RadiologyCDM_Kor
6 E. Y. KWON, C.-W. Jeong, D. M. Kang, Y. R. Kim, Y. H. Lee, and K.-H. Yoon, "Development of Common Data Module Extension for Radiology Data (R_CDM): A Pilot Study to Predict Outcome of Liver Cirrhosis with using portal Phase Abdominal Computed Tomography Data," ECR 2019, 10.26044/ecr2019/C-1876.
7 R.W. Park, "The Distributed Research Network, Observational Health Data Sciences and Informatics, and the South Korean Research Network," The Korean Journal of Medicine, Vol.94, No.4, pp.309-314, 2019.   DOI
8 W. D. Bidgood Jr., S. C. Horii, F. W. Prior, and D. E. Van Syckle, "Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging," Journal of the American Medical Informatics Association, Vol.4, No.3, pp.199-212, May-Jun. 1997.   DOI
9 A. V. Dalca, K. L. Bouman, and W. T. Freeman, N. S. Rost, M. R. Sabuncu, P. Golland, "Medical Image Imputation From Image Collections," IEEE Transactions on Medical Imaging, Vol.38, No.2, pp.504-514, Feb. 2019.   DOI
10 Marc D., Kohli, Ronald M. Summers, and J. Raymond Geis, "Medical Image Data and Datasets in the Era of Machine Learning-whitepaper from the 2016 C-MIMI Meeting Dataset Session," Journal of Digital Imaging, Vol.30, No.4, pp.392-399, 2017.   DOI
11 M. G. Pak, S. M. Han, C. S. Lee, C. W. Jeong, and K. H. Yoon, "Medical Dataset Preparation Platform Based on a Common Data Model for Machine Learning," Test Engineering and Management, Vol.81, pp.2410-2415, 2019.
12 DEEP NOID Solutions [Internet], https://www.deepnoid.com/solutions
13 Sung-Uk Park, "Keyword Analysis of Data Technology Using Big Data Technique," Journal of Korea Technology Innovation Society, Vol.2, No.2, pp.265-281, 2019.   DOI
14 Presidential committee on the Fourth Industrial Revolution, https://www.4th-ir.go.kr/