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2,141 results on '"Kim, Jin-Wook"'

3. Central neurocytoma exhibits radial glial cell signatures with FGFR3 hypomethylation and overexpression

Author

Lee, Yeajina, Chowdhury, Tamrin, Kim, Sojin, Yu, Hyeon Jong, Kim, Kyung-Min, Kang, Ho, Kim, Min-Sung, Kim, Jin Wook, Kim, Yong-Hwy, Ji, So Young, Hwang, Kihwan, Han, Jung Ho, Hwang, Jinha, Yoo, Seong-Keun, Lee, Kyu Sang, Choe, Gheeyoung, Won, Jae-Kyung, Park, Sung-Hye, Lee, Yong Kyu, Shin, Joo Heon, Park, Chul-Kee, Kim, Chae-Yong, and Kim, Jong-Il

Published
2024
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14. Baseline Viral Load and On-treatment Hepatocellular Carcinoma Risk in Chronic Hepatitis B: A Multinational Cohort Study

Author

Choi, Won-Mook, Yip, Terry Cheuk-Fung, Wong, Grace Lai-Hung, Kim, W. Ray, Yee, Leland J., Brooks-Rooney, Craig, Curteis, Tristan, Clark, Laura J., Jafry, Zarena, Chen, Chien-Hung, Chen, Chi-Yi, Huang, Yi-Hsiang, Jin, Young-Joo, Jun, Dae Won, Kim, Jin-Wook, Park, Neung Hwa, Peng, Cheng-Yuan, Shin, Hyun Phil, Shin, Jung Woo, Yang, Yao-Hsu, and Lim, Young-Suk

Published
2024
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26. Hepatocellular carcinoma risk in patients with chronic hepatitis B receiving tenofovir- vs. entecavir-based regimens: Individual patient data meta-analysis

Author

Choi, Won-Mook, Yip, Terry Cheuk-Fung, Wong, Grace Lai-Hung, Kim, W. Ray, Yee, Leland J., Brooks-Rooney, Craig, Curteis, Tristan, Cant, Harriet, Chen, Chien-Hung, Chen, Chi-Yi, Huang, Yi-Hsiang, Jin, Young-Joo, Jun, Dae Won, Kim, Jin-Wook, Park, Neung Hwa, Peng, Cheng-Yuan, Shin, Hyun Phil, Shin, Jung Woo, Yang, Yao-Hsu, and Lim, Young-Suk

Published
2023
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34. The telomere maintenance mechanism spectrum and its dynamics in gliomas

Author

Kim, Sojin, Chowdhury, Tamrin, Yu, Hyeon Jong, Kahng, Jee Ye, Lee, Chae Eun, Choi, Seung Ah., Kim, Kyung-Min, Kang, Ho, Lee, Joo Ho, Lee, Soon-Tae, Won, Jae-Kyung, Kim, Kyung Hyun, Kim, Min-Sung, Lee, Ji Yeoun, Kim, Jin Wook, Kim, Yong-Hwy, Kim, Tae Min, Choi, Seung Hong, Phi, Ji Hoon, Shin, Young-Kyoung, Ku, Ja-Lok, Lee, Sungyoung, Yun, Hongseok, Lee, Hwajin, Kim, Dokyoung, Kim, Kyoungmi, Hur, Junho K., Park, Sung-Hye, Kim, Seung-Ki, and Park, Chul-Kee

Published
2022
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39. Unveiling the long-term degradation mechanisms of the sealing structures for durable PEM fuel cells by ex situ accelerated stability evaluation of a membrane electrode assembly.

Author

Kim, Jin-Wook, Yang, Sooyoung, Shin, Gyu Jin, Oh, Min Jeong, Lee, Keumjung, and Lee, Jun Hyup

Abstract

The membrane electrode assembly (MEA) in proton exchange membrane fuel cells (PEMFCs) should be sealed with a robust frame for the long-term stability and performance of the fuel cell stacks. In this study, we investigated the long-term degradation mechanism of MEA frames containing proton-exchange membranes under the influence of various acceleration factors present in an actual fuel-cell operating environment using an ex situ accelerated lifespan evaluation method. The stability of the membrane-containing MEA frames is evaluated in terms of peel strength. We quantified the effects of water, temperature, and acidity, which are the main factors in an actual operating environment, on the long-term degradation of the MEA frame. Long-term exposure of the MEA frames to high temperatures in aqueous environments caused significant deterioration of their long-term durability. This deterioration was attributed to the weakening of the cohesive interactions between the ionomer chains in the Nafion membrane, which was accelerated in a strongly acidic environment. Furthermore, high temperatures and lower pH levels enhanced the glass transition of the ionomers and significantly increased the ion cluster area, increasing the free volume of the membrane layer and consequently reducing the long-term stability of the MEA frame. This study elucidated the underlying failure mechanisms of MEA frames containing membranes during fuel-cell operation and highlighted the influence of specific acceleration factors on the degradation of the frame-sealing structure, providing important guidelines for future consideration. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Selection of Convolutional Neural Network Model for Bladder Tumor Classification of Cystoscopy Images and Comparison with Humans.

Author

Lee, Ju Young, Lee, Yong Seong, Tae, Jong Hyun, Chang, In Ho, Kim, Tae-Hyoung, Myung, Soon Chul, Nguyen, Tuan Thanh, Lee, Jae Hyeok, Choi, Joongwon, Kim, Jung Hoon, Kim, Jin Wook, and Choi, Se Young

Subjects
ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, MEDICAL students, ARTIFICIAL intelligence
Abstract

Purpose: An investigation of various convolutional neural network (CNN)-based deep learning algorithms was conducted to select the appropriate artificial intelligence (AI) model for calculating the diagnostic performance of bladder tumor classification on cystoscopy images, with the performance of the selected model to be compared against that of medical students and urologists. Methods: A total of 3,731 cystoscopic images that contained 2,191 tumor images were obtained from 543 bladder tumor cases and 219 normal cases were evaluated. A total of 17 CNN models were trained for tumor classification with various hyperparameters. The diagnostic performance of the selected AI model was compared with the results obtained from urologists and medical students by using the receiver operating characteristic (ROC) curve graph and metrics. Results: EfficientNetB0 was selected as the appropriate AI model. In the test results, EfficientNetB0 achieved a balanced accuracy of 81%, sensitivity of 88%, specificity of 74%, and an area under the curve (AUC) of 92%. In contrast, human-derived diagnostic statistics for the test data showed an average balanced accuracy of 75%, sensitivity of 94%, and specificity of 55%. Specifically, urologists had an average balanced accuracy of 91%, sensitivity of 95%, and specificity of 88%, while medical students had an average balanced accuracy of 69%, sensitivity of 94%, and specificity of 44%. Conclusions: Among the various AI models, we suggest that EfficientNetB0 is an appropriate AI classification model for determining the presence of bladder tumors in cystoscopic images. EfficientNetB0 showed the highest performance among several models and showed high accuracy and specificity compared to medical students. This AI technology will be helpful for less experienced urologists or nonurologists in making diagnoses. Image-based deep learning classifies bladder cancer using cystoscopy images and shows promise for generalized applications in biomedical image analysis and clinical decision making. [ABSTRACT FROM AUTHOR]

Published
2024
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