Abstract
Recent advances in artificial intelligence have been used for a variety of clinical interests, such as lesion detection, segmentation, classification, and differentiation in medical images, and a great deal of research is underway. A good algorithm is also needed to obtain meaningful artificial intelligence research results, but a correctly created data set is also important. To provide these data sets, we proposed the radiology-common data model (R-CDM), as an expansion of OMOP-CDM. With the use of R-CDM, this study created a data set to distinguish urinary diseases for 873 patients and showed significance using an artificial intelligence algorithm that modified GoogleNet, a deep-learning algorithm based on CNN. In this experiment, 99% accuracy and AUROC 0.9 were obtained. External validation was performed using urinary stones data sets for 200 patients at other hospitals to increase confidence in the results of deep learning. In external validation, the results of 90% sensitivity, 97% specificity, and AUROC 0.984 confirm that the data sets and algorithms have very high efficiency. The study will help researchers who perform artificial intelligence through medical imaging.
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Acknowledgments
This study was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (HI18C1216) and the Technology Innovation Program (or Industrial Strategic Technology Development Program(20001234) and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.2018R1D1A1B07048833).
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Noh, SH. et al. (2021). Analysis and Classification of Urinary Stones Using Deep Learning Algorithm: A Clinical Application of Radiology-Common Data Model (R-CDM) Data Set. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_60
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DOI: https://doi.org/10.1007/978-3-030-55190-2_60
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