About Me
Sihwan (Raphael)
IT-Loving Scientist
My scholarly endeavors have been directed towards the advancement of automated AI models dedicated to the extracting digital imaging-biomarkers from clinical data acquired in real-world settings.
This pursuit includes the formulation of advanced methodologies for clustering extensive information derived from multimodal longitudinal clinical data, with the objective of modeling medical conditions and risk factors, and predicting phenotypes as well as the trajectories of disease progression.
My current research interests are as follows:
1) Clinically Optimized Medical Image Processing
2) Medical Physics-based Artificial Intelligence on Clinical Imaging
3) Nano-Biological Imaging
Outside of research, I enjoys creative designing, playing tennis, and traveling.
My current research interests are as follows:
1) Clinically Optimized Medical Image Processing
2) Medical Physics-based Artificial Intelligence on Clinical Imaging
3) Nano-Biological Imaging
Outside of research, I enjoys creative designing, playing tennis, and traveling.
Featured Publications
Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT
Journal
European Radiology
2025
Long-Term Prognostic Implications of Thoracic Aortic Calcification on CT Using Artificial Intelligence–Based Quantification in a Screening Population: A Two-Center Study
Journal
American Journal of Roentgenology
2025
Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program
Journal
PLoS ONE
2022
Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study
Journal
PLoS ONE
2022
Featured Projects
Development and clinical validation of opportunistic health screening solutions in CT scan
Current
Mar. 2022 - Now
Developing AI system designed to assist clinicians carry out precise diagnosis and investigating a new imaging biomarkers.
Development and clinical validation of an AI-assisted overscan audit algorithm in routine low-dose chest CT screening
Completed
Jan. 2021 - Aug. 2022
The project developed and validated overscan decision algorithm enabling the retrospective scan range monitoring in LDCT for lung cancer screening program.