- Randomized Clinical Trials of Artificial Intelligence in Medicine: Why, When, and How?
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Park SH, Choi JI, Fournier L, Vasey B
- KMID: 2536339
- Korean J Radiol.
- 2022 Dec;23(12):1119-1125.
- doi: 10.3348/kjr.2022.0834
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- 2022 KLCA-NCC Korea Practice Guidelines for the Management of Hepatocellular Carcinoma
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Park JW
- KMID: 2536340
- Korean J Radiol.
- 2022 Dec;23(12):1126-1240.
- doi: 10.3348/kjr.2022.0822
Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the fourth most common cancer among men in South Korea, where the prevalence of chronic hepatitis B infection is... -
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- Use of “Diagnostic Yield” in Imaging Research Reports: Results from Articles Published in Two General Radiology Journals
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Park HY, Suh CH, Kim SO
- KMID: 2536346
- Korean J Radiol.
- 2022 Dec;23(12):1290-1300.
- doi: 10.3348/kjr.2022.0741
Objective: “Diagnostic yield,” also referred to as the detection rate, is a parameter positioned between diagnostic accuracy and diagnosis-related patient outcomes in research studies that assess diagnostic tests. Unfamiliarity with... -
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- Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm
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Chang S, Han K, Lee S, Yang YJ, Kim PK, Choi BW, Suh YJ
- KMID: 2536342
- Korean J Radiol.
- 2022 Dec;23(12):1251-1259.
- doi: 10.3348/kjr.2022.0496
Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated... -
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- Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study
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Kim YS, Jang Mj, Lee SH, Kim SY, Ha SM, Kwon BR, Moon WK, Chang JM
- KMID: 2536341
- Korean J Radiol.
- 2022 Dec;23(12):1241-1250.
- doi: 10.3348/kjr.2022.0263
Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and... -
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- Cutoff Values for Diagnosing Hepatic Steatosis Using Contemporary MRI-Proton Density Fat Fraction Measuring Methods
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Park S, Kwon JH, Kim SY, Kang JH, Chung JI, Jang JK, Jang HY, Shim JH, Lee SS, Kim KW, Song GW
- KMID: 2536343
- Korean J Radiol.
- 2022 Dec;23(12):1260-1268.
- doi: 10.3348/kjr.2022.0334
Objective: To propose standardized MRI-proton density fat fraction (PDFF) cutoff values for diagnosing hepatic steatosis, evaluated using contemporary PDFF measuring methods in a large population of healthy adults, using histologic... -
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- Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI
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Heo S, Lee SS, Kim SY, Lim YS, Park HJ, Yoon JS, Suk HI, Sung YS, Park B, Lee JS
- KMID: 2536344
- Korean J Radiol.
- 2022 Dec;23(12):1269-1280.
- doi: 10.3348/kjr.2022.0494
Objective: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation... -
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- Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy
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Kim KM, Hwang H, Sohn B, Park K, Han K, Ahn SS, Lee W, Chu MK, Heo K, Lee SK
- KMID: 2536345
- Korean J Radiol.
- 2022 Dec;23(12):1281-1289.
- doi: 10.3348/kjr.2022.0539
Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and... -
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