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Young-Min Kwon, M.D., Ph.D.


Available: 01/10/22, Expires: 12/31/24

Recent total knee arthroplasty (TKA) designs preserve or retain both the anterior and posterior cruciate ligaments (ACL and PCL) in order to enhance more physiological tibiofemoral kinematics and maintain proprioception. These implants include newly introduced non-modular femoral design of Bi-Cruciate Retaining (BCR) Total Knee Arthroplasty. Although preservation of the anterior and posterior cruciate ligament is thought to be necessary to achieve more normal kinematics, the effect of the ACL and PCL on knee joint kinematics after bicruciate-retaining knee arthroplasties may differ from that in the native knee as the articular surface in the tibiofemoral joint is altered following these knee arthroplasties. However, in vivo kinematics including six degrees of freedom (6DOF) and tibiofemoral articular contact motion of the knee in patients with ACL-PCL retaining knee arthroplasties has not been previously quantified. The research goal is quantification and comparison of 3 TKA designs (Cruciate Retaining (CR), Posterior Cruciate Substituting (PS) TKA, Bi-Cruciate Retaining (BCR) Total Knee Arthroplasty surgical system) in comparison with wellfunctioning contralateral knee in restoring normal in vivo knee kinematics during functional activity using validated Dual Fluoroscope Imaging System. In addition, machine learning (artificial neural network) will be utilized to assess the predictive values of in vivo kinematics. The responsibilities of the student at the Orthopaedic Bioengineering Lab, Massachusetts General Hospital will include quantitative investigation of the in vivo hip and knee biomechanics of patients with total joint replacements. Specifically, the research fellow will process CT and MRI images of patients to construct novel 3D anatomic models of the hip and knee and examine the optimal surgical implantation of joint replacements. This includes data gathering, analysis, and preparation of abstracts/manuscripts as well as presentation at national professional meeting. The student will work with a multidisciplinary team of engineers, surgeons in conducting human joint kinematics and kinetics evaluation using imaging and motion analysis techniques as machine learning (artificial neural network) will be utilized to assess the predictive values of in vivo kinematics. .

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Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.