Veritas in Scientia

Research Projects

Development and clinical validation of opportunistic health screening solutions in CT scan
Current

Mar. 2022 - Now

Artificial Intelligence in Radiology

AI
Knowledge Graph
Literature Mining
Research Tools

Developing AI system designed to assist clinicians carry out precise diagnosis and investigating a new imaging biomarkers.

Collaborators: S. Kim (Ph.D.) , J.H. Kim (Ph.D.) , E.A. Park (M.D.&Ph.D.) , B.J. Kang (M.D.) , J.E. Lee (M.D.&Ph.D.) , Y.J. Seo (M.D.&Ph.D.)
S. Kim et al. (2025), Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT, European Radiology J. Lee et al. (2025), Long-Term Prognostic Implications of Thoracic Aortic Calcification on CT Using Artificial Intelligence–Based Quantification in a Screening Population: A Two-Center Study, American Journal of Roentgenology B.J. Kang et al. (2024), Clinical Application of Artificial Intelligence-Based Computed Tomography Analysis of Myosteatosis in Localized Renal Cell Carcinoma, Journal of Urologic Oncology

Developing AI-assisted Seg-Hallucination audit algorithm for evaluating the reliability of AI-based segmentation
Current

Jan. 2023 - Now

Artificial Intelligence in Medical Image Processing

AI-audit
Seg-Hallucination
Uncertainty
Anormaly Screening
Segmentation

The project explores automated strategy for auditing seg-hallucination in medical image segmentation task.

Collaborators: S. Kim (Ph.D.) , J.H. Kim (Ph.D.) , C. Park (M.S.) , S.H. Kim (M.S. Student)
S. Kim et al. (2025), Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI, Bioengineering

Development and clinical validation of an AI-assisted overscan audit algorithm in routine low-dose chest CT screening
Completed

Jan. 2021 - Aug. 2022

Aritificial Intelligence in Radiology

AI-audit
Deep Learning
Low-dose chest CT
Lung Cancer Screening
Overscan

The project developed and validated overscan decision algorithm enabling the retrospective scan range monitoring in LDCT for lung cancer screening program.

Collaborators: S. Kim (Ph.D.) , J.H. Kim (Ph.D.) , M. Chun (Ph.D.) , J.H. Choi (M.D.) , W.K. Joeng (M.D.&Ph.D.)
S. Kim et al. (2022), Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program, PLoS ONE

Development and clinical validation of an AI-assisted automated algorithm for image quality assessment in routine low-dose CT scan
Completed

Jan. 2021 - Aug. 2022, 2025

Aritificial Intelligence in Radiology

Image Qaulity Assessment
Deep Learning
CT
Structure Coherence Feature

A computational tool for predicting protein 3D structures using deep learning methods.

Collaborators: S. Kim (Ph.D.) , M. Chun (Ph.D.) , J.H. Kim (Ph.D.) , C. Ahn (Ph.D.) , C. Park (M.S.) , J.H. Choi (M.D.) , W.K. Jeong (M.D.&Ph.D.)
C. Park et al. (2025), Noise power spectrum analysis in CT for improved patient-specific image optimization: a shift from phantom model to clinical scan, SPIE Medical Imaging M. Chun et al. (2022), Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study, PLoS ONE S. Kim et al. (2021), Estimation of Noise Level and Edge Preservation for Computed Tomography Images: Comparisons in Iterative Reconstruction, Progress in Medical Physics

Designing a 3D-printed Anthropomorphic CT-phantom having Covid-19 lung disease
Completed

Jan. 2020 - Dec. 2021

Aritificial Intelligence in Radiology

CT-phantom
Anthropomorphic
COVID-19
3D-printing
AI-assisted Diagnostic Tools

Development of 3D-printed anthropomorphic CT-phantom having Covid-19 lung disease for evaluating the performance of AI-assisted diagnostic tools.

Collaborators: S. Kim (Ph.D.) , J.H. Kim (Ph.D.)
[Project Slides] Fabrication and Application of 3D-Printed Anthropomorphic CT Phantom for Evaluating AI-Assisted Diagnostic Tools

Enhancing radiation oncology workflow with AI-assisted radiation therapy planning
Completed

Jan. 2019 - Dec. 2019

Aritificial Intelligence in Radiation Oncology

Deep Learning
Radiation Theraphy Planning
AI Contouring
Tumor Tracking
Organ at Risk

Development of deep learning-based AI-assisted radiation therapy planning system for enhancing radiation oncology workflow.

Collaborators: S. Kim (Ph.D.) , J.H. Kim (Ph.D.) , E.K. Chie (M.D.&Ph.D.)