Yongsik Sim

Clinical assistant professor

Yonsei University College of Medicine

South Korea

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Yongsik Sim is a Clinical Assistant Professor at Yonsei University College of Medicine in South Korea. His research primarily focuses on neuro-oncology, machine learning applications in medical imaging, and the prognostic implications of imaging biomarkers in various neurological disorders. Recent articles include analyses of H3 K27-altered diffuse midline gliomas and investigations into the effects of deep learning on radiological performance. He is dedicated to advancing the fields of clinical imaging and personalized medicine.

Yongsik Sim

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Articles (23)

Effect of Deep Learning-Based Artificial Intelligence on Radiologists’ Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging

Leber Hereditary Optic Neuropathy and Stroke-Like Lesions Associated With MT-ND5 m.13513G>A Mutation: A Case Report

A comprehensive multicenter analysis of clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma in adults

Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

MRI‐Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic–Clonic Seizures Alone

Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques

Radiomics and PD-L1 expression predict immunotherapy benefits in patients with head and neck squamous cell carcinoma

Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization

Revisiting prognostic factors of gliomatosis cerebri in adult-type diffuse gliomas

Revisiting gliomatosis cerebri in adult-type diffuse gliomas: a comprehensive imaging, genomic and clinical analysis

Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test

A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study

Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery

Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty

Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

A Radiomics Approach for the Classification of Fibroepithelial Lesions on Breast Ultrasonography

A RADIOMICS APPROACH FOR THE CLASSIFICATION OF FIBROEPITHELIAL LESIONS ON BREAST ULTRASONOGRAPHY

Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

Collaborators (10)

Yae Won Park

Eung Yeop Kim

Jong Yun Won

Sung Tae Kim

Sungwan Kim

Sungwon Kim

Man-deuk Kim

Na-Young Shin

Dan Yoon

Beomseok Sohn

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