Sungkyunkwan University · 医学
Professor Hyunggun Kim's research lab specializes in biomedical engineering and computational biomechanics, focusing on the development of personalized computational models for heart valve function and disease. The lab integrates patient-specific medical imaging, advanced finite element analysis, and innovative biomaterials to study valve mechanics, tissue degeneration, and tissue engineering applications. Research also extends to agricultural machine vision using weakly supervised deep learning and in vivo imaging techniques for early detection of vascular pathologies.
Figures are computed from collected data and may differ slightly.
While providing nearly trouble-free function for 10-12 years, current bioprosthetic heart valves (BHV) continue to suffer from limited long-term durability. This is usually a result of leaflet calcification and/or structural degeneration, which may be related to regions of stress concentration associated with complex leaflet deformations. In the current work, a dynamic three-dimensional finite element analysis of a pericardial BHV was performed with a recently developed FE implementation of the
Posterior leaflet prolapse following chordal elongation or rupture is one of the primary valvular diseases in patients with degenerative mitral valves (MVs). Quadrangular resection followed by ring annuloplasty is a reliable and reproducible surgical repair technique for treatment of posterior leaflet prolapse. Virtual MV repair simulation of leaflet resection in association with patient-specific 3D echocardiographic data can provide quantitative biomechanical and physiologic characteristics of
Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less labor
This study demonstrates specific highlighting of early/inflammatory atheroma in vivo using anti-ICAM-1 ELIP. Three-dimensional IVUS reconstruction provides good visualization of plaque distribution in the arterial wall. This novel methodology may help to detect and diagnose pathophysiologic development of all stages of atheroma formation in vivo and quantitate plaque volume for serial and long-term atherosclerotic treatment studies.
A biocomposite scaffold supplemented with collagen extracted from fish skin and phlorotannin from brown algae was proposed.
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