Hyungkeon Kim
Sungkyunkwan University · Medicine
About the Lab
Professor Hyungkeon Kim's research lab specializes in biomedical engineering and computational biomechanics, focusing on the development of patient-specific simulation models for heart valve diseases and their surgical repair. The lab integrates advanced imaging, finite element analysis, and personalized computational modeling to study the biomechanical mechanisms of valvular dysfunction, such as leaflet prolapse and calcification. Additionally, the lab applies cutting-edge technologies like machine learning and deep learning to medical diagnostics, including fall detection systems and agricultural image segmentation, while also contributing to molecular and vascular imaging for early atherosclerosis detection.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15While 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
A simple and rapid liquid chromatography tandem mass spectrometry method has been developed for the determination of BH4, DA, 5-HT, NE, EP, Glu, and GABA in mouse brain using epsilon-acetamidocaproic acid and isotopically labeled neurotransmitters as internal standards. Proteins in the samples were precipitated by adding acetonitrile, and then the supernatants were separated by a Sepax Polar-Imidazole (2.1 mm × 100 mm, i.d., 3 μm) column by adding a mixture of 10 mM ammonium formate in acetonitr
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
In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male su
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.
Research Areas
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