Kyung Hee University · 歯学
Professor Seong-Hun Kim's research lab specializes in digital and biomaterials-driven advancements in oral and maxillofacial surgery and orthodontics. The lab focuses on the development of 3D imaging-guided orthodontic treatment systems, including automated cephalometric landmark detection using deep learning, and the design of customized orthodontic appliances through CBCT and 3D dental model integration. A key research direction involves the biomechanical evaluation of dental implants—particularly SLA-surface mini-implants—toward optimizing osseointegration and treatment outcomes. Additionally, the lab investigates molecular mechanisms of gamma-secretase complex formation, particularly the structural interactions between presenilin and PEN-2 in Alzheimer’s disease-related pathways.
Figures are computed from collected data and may differ slightly.
SLA mini-implants showed relatively lower insertion torque value and angular momentum and higher total energy during removal than the machined implants, suggesting osseointegration of the SLA mini-implant after insertion.
Macromolecular complexes containing presenilins (PS1 and PS2), nicastrin, anterior pharynx defective phenotype 1 (APH-1), and PS enhancer 2 (PEN-2) mediate the intramembranous, gamma-secretase cleavage of beta-amyloid precursor protein (APP), Notch, and a variety of type 1 membrane proteins. We previously demonstrated that PEN-2 is critical for promoting endoproteolysis of PS1 and that the proximal two-thirds of transmembrane domain (TMD) 1 of PEN-2 is required for binding with PS1. In this stud
Macromolecular complexes containing presenilins (PS), nicastrin (NCT), APH-1, and PEN-2 mediate the gamma-secretase cleavage of the beta-amyloid precursor protein and Notch. APH-1 and NCT stabilize the PS1 holoprotein, whereas PEN-2 is critical for endoproteolysis of PS1. To define the structural domains of PEN-2 that are necessary for mediating PS1 endoproteolysis and gamma-secretase activity, we coexpressed APH-1, NCT, and PS1 together with a series of PEN-2 mutants, which harbored deletions i
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preproce
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