Yonghwan Kim on Muck Rack

Yonghwan Kim

United States
As seen in: Nature, Wiley Online Library, MDPI, Ocean Engineering, SAGE Publishing, Taylor & Francis Online, Royal Society of Chemistry, Emerald Publishing, Dialnet - Universidad de La Rioja, News Ghana and

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Articles

Constructing a Synergistic Triple Layer Interfacial Design for Dendrite-Free and High-Performance Lithium Metal Batteries Click to copy article link Article link copied!

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By Yonghwan Kim, Yujin Chang, Kyurim Kim
| dx.doi.org
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Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer

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By Yonghwan Kim, Hyein Ju, Hyun Jun Im, Hyun Kim, Seungun Lee, Yun Ji Nam, Hyungu Kwon, Siwon Lee, Dabin Lee, Eunyoung Park, sung kim, Inkeun Park, Jae Lee, Bumsik Hong, Yong Mee Cho, Dong-Myung Shin, Jinahn Jeong, Gowun Jeong, Ja-Min Park, Min Gi Jang
| Nature Verified
Abstract Muscle-invasive bladder cancer (MIBC) presents with variable clinical and pathological features, leading to inconsistent responses to standard treatments such as neoadjuvant chemotherapy (NAC). Although transcriptome profiling has shown differences in NAC response, reliable predictors of treatment outcome remain elusive.
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Artificial intelligence-based prediction of radio-cephalic arteriovenous fistula maturation using preoperative duplex examination: a retrospective multicenter cohort study

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By Ara Cho, Sanghyun Ahn, Jang Yong Kim, Yonghwan Kim, Hyangkyoung Kim, JungKyun Hyun
| Nature Verified
Abstract Establishing a functional arteriovenous fistula (AVF) is crucial to ensure effective dialysis treatment. However, there are currently no definitive criteria for predicting AVF maturation using preoperative ultrasound. This study aims to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation.
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