Seungbo Lee

ORCID: 0000-0001-6811-917X
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About
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Research Areas
  • Ultrasound in Clinical Applications
  • Bone and Joint Diseases
  • Traditional Chinese Medicine Analysis
  • Cardiac Imaging and Diagnostics
  • Natural product bioactivities and synthesis
  • Musculoskeletal synovial abnormalities and treatments
  • Bone Tumor Diagnosis and Treatments
  • Venous Thromboembolism Diagnosis and Management
  • Advanced Neuroimaging Techniques and Applications
  • Cervical and Thoracic Myelopathy
  • Phytochemistry and Biological Activities
  • Radiomics and Machine Learning in Medical Imaging
  • Sarcoma Diagnosis and Treatment

Seoul National University Hospital
2022

University of Ulsan
2021

Asan Medical Center
2021

Ulsan College
2021

Kyung Hee University
2018

Gangnam Severance Hospital
2015

Yonsei University
2015

Objective: To assess the performance of diffusion tensor imaging (DTI) for diagnosis cervical spondylotic myelopathy (CSM) in patients with deformed spinal cord but otherwise unremarkable conventional magnetic resonance (MRI) findings.Materials and Methods: A total 33 who underwent MRI spine including DTI using twodimensional single-shot interleaved multi-section inner volume diffusion-weighted echo-planar whose cords were showed no signal changes on subjects this study.Mean diffusivity...

10.3348/kjr.2015.16.6.1303 article EN cc-by-nc Korean Journal of Radiology 2015-01-01

Background Diagnostic performance, inter-observer agreement, and intermodality agreement between computed tomography (CT) magnetic resonance imaging (MRI) in the depiction of major distinguishing features central cartilaginous tumors have not been investigated. Purpose To determine CT MRI evaluation appendicular bones, to compare their diagnostic performance. Material Methods Two independent radiologists retrospectively reviewed preoperative MRI. Inter-observer assessment features, including...

10.1177/0284185121996268 article EN Acta Radiologica 2021-02-27

Abstract Pulmonary thromboembolism (PTE) is one of the most important complications in gastrointestinal cancer patients. However, there were few studies that predict pulmonary embolism using machine learning (ML). The purpose this study was to develop an ML based prediction model for PTE patients, and compare its performance with conventional model. In a tertiary hospital, patients who underwent computed tomographic angiography (CTPA) reviewed retrospectively from 2010 2020. Demographic...

10.21203/rs.3.rs-1635342/v2 preprint EN cc-by Research Square (Research Square) 2022-09-27
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