Dr Minsuok Kim

BS, MS, MS, PhD

  • Lecturer in Biomedical Engineering
Publications
Headshot of Minsuok Kim, looking directly at the camera.

Background

Dr Minsuok Kim has multidisciplinary study and research experience in science, engineering, and healthcare technology. He obtained his PhD from the State University of New York at Buffalo for Mechanical and Aerospace Engineering in 2007.

The topic of his thesis was computational modelling of neurovascular haemodynamics and endovascular intervention devices. His research interest in biomedical engineering led him to join a European brain aneurysm research project, @neurIST. Through this project, he developed a computational model to simulate neurovascular haemodynamics and endovascular treatment.

Later, he went to the University of Oxford to join multi-scale respiratory system modelling projects, AirPROM and Synergy-COPD. During these projects, he studied respiratory dynamics and developed a full-scale flow model based on medical images. He developed his modelling technique later, and the results enabled the functional assessment of a lung using conventional structural CT scans.

Qualifications and awards

  • PhD, Mechanical and Aerospace Engineering, State University of New York at Buffalo, USA 
  • MS, Environmental and Health Science, University of Michigan, USA 
  • MS, Civil Engineering, Seoul National University, South Korea 
  • BS, Physics, Hankuk University of Foreign Studies, South Korea 
 

 

Main research interests

Minsuok's current research interests lie in medical image-based respiratory system modelling and its application to develop a medical image-based artificial intelligence algorithm to support respiratory disease diagnosis. 

  • Medical image-based respiratory airway flow modelling:Lung disease is a leading cause of mortality and morbidity with a substantial worldwide economic and social burden. Thoracic computed tomography (CT) is a clinically established diagnostic technique to detect structural pulmonary abnormalities. Although CT is routinely performed clinically, it provides no functional pulmonary information. Furthermore, the structural evaluation from CT does not predict the rate of decline of an individual’s health or COPD prognosis. My research interests include the development of medical image-based computational modelling techniques to simulate respiratory flow dynamics and to improve clinical diagnosis by providing accurate assessments of lung function. 
  • Artificial intelligence system to support medical image analysis: Artificial intelligence (AI) is one of the main factors driving future technology development. The application of AI in clinical assessment is expected to improve diagnostic and post-operative outcomes drastically. In this mainstream, I would like to apply the current computational modelling techniques to introduce an advanced AI system. This approach will facilitate the development of a smarter AI algorithm, analysing medical images to elucidate the disease-related factors beyond human capability. 

Grants and contracts

  • 2022 BIOREME Small Starter Grant 
  • 2015-2016 Warwick Impact Fund, University of Warwick 

 

 

 

 

 

Current teaching responsibilities

  • WSA102 Engineering Science 1 
  • WSC301 Software Engineering 
  • WSC802 Computational Fluid Dynamics1 
  • WSD802 Computational Fluid Dynamics2 
  • WSP830 ThermoFluid 

 

External collaborators 

  • University of Oxford, Oxford, UK 
  • University of Warwick, Coventry, UK 
  • Asan Hospital, Seoul, South Korea 
  • Seoul National University, Seoul, South Korea 
  • Tokyo University, Tokyo, Japan 
  • Kyoto Institute of Technology, Kyoto, Japan