Explore this Laboratory
The Orthopaedic Bioengineering Laboratory, led by Harvard Medical School Professor of Orthopedic Surgery and Vice-chair of the Department of Orthopaedic Surgery at Mass General, Young-Min Kwon, MD, PhD, is renowned for its long-standing tradition of excellence in innovative hip and knee arthroplasty. Established in 1998, the laboratory’s focus is on improving patient outcomes through the novel application of robotics to the evaluation of human joints. In 2003, the Lab patented a Dual Fluoroscopic Imaging System (DFIS) that accurately determines six degrees of freedom (6DOF) for in vivo musculoskeletal joint kinematics. This innovation led to a wellspring of groundbreaking research on the in vivo functionality of joint replacement implants during routine activity. The laboratory continues to focus on translational orthopaedic research, with a focus on refining total hip and knee arthroplasty to optimize patient outcomes, as well as leveraging Artificial Intelligence (AI) to analyze and predict complications associated with joint arthroplasty.
In vivo kinematic analysis of the knee joint following total knee arthroplasty using 2D to 3D mapping.
- Anirudh Buddhiraju, MD
- Tony Lin-Wei Chen, MD, PhD
- Ziwei Huang, MD, PhD
- Shane Fei Chen, PhD
- Mohammad Amin Rezazadeh Saatlou, MD
- Anzar Sarfraz, MD
- Michelle Shimizu
- Henry Hojoon Seo
- Blake Bacevich
- Jona Kerluku
- Buddhiraju A, Chen TLW, Subih M, Seo HH, Esposito JG, Kwon YM. Validation and generalizability of machine learning models for the prediction of discharge disposition following revision total knee arthroplasty. J Arthroplasty 2023;38(6):253-258.
- Chen TLW, Buddhiraju A, Seo HH, Subih M, Tuchinda P, Kwon YM. Internal and external validation of the generalizability of machine learning algorithms in predicting non-home discharge disposition following primary total knee joint arthroplasty. J Arthroplasty 2023;S0883-5403(23)00085-2.
- Ingall E, Klemt C, Melnic CM, Cohen-Levy WB, Tirumala V, Kwon YM. Impact of preoperative opioid use on patient-reported outcomes after revision total knee arthroplasty: A propensity matched analysis. J Knee Surg 2023;36(2):115-120.
- Yeo I, Klemt C, Melnic CM, Pattavina MH, De Oliveira BMC, Kwon YM. Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models. Arch Orthop Trauma Surg. 2023;143(6):3299-3307.
- Klemt C, Bounajem G, Tirumala V, Xiong L, Oganesyan R, Kwon YM. Posterior tibial slope increases anterior cruciate ligament stress in bi-cruciate retaining total knee arthroplasty: in vivo kinematic analysis. J Knee Surg. 2022;35(7):788-797.
- Klemt C, Chen W, Bounajem G, Tirumala V, Xiong L, Kwon YM. Outcome and risk factors of failures associated with revision total hip arthroplasty for recurrent dislocation. Arch Orthop Trauma Surg. 2022;142(8):1801-1807.
- Klemt C, Harvey MJ, Robinson MG, Yeo I, Esposito JG, Kwon YM. Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2022;30(8):2573-2581.
- Klemt C, Padamanabha A, Tirumala V, Smith EJ, Kwon YM. The effect of joint line elevation on in-vivo knee kinematics in bi-cruciate retaining total knee arthroplasty. J Knee Surg. 2022;35(13):1445-1452.
- Klemt C, Uzosike AC, Cohen-Levy WB, Harvey MJ, Subih MA, Kwon YM. The ability of deep learning models to identify total hip and knee arthroplasty implant design from plain radiographs. J Am Acad Orthop Surg. 2022;30(9):409-415.
- Kwon YM, Klemt C. Metal articulations as a source of total hip arthroplasty pain. J Arthroplasty. 2022;37(8):1483-1487.
- Oganesyan R, Klemt C, Esposito J, Tirumala V, Xiong L, Kwon YM. Knee arthroscopy prior to revision TKA is associated with increased re-revision for stiffness. J Knee Surg. 2022;35(11):1223-1228.
The Bioengineering Lab is looking to recruit an additional Postdoctoral Research Fellow to join our research team. Responsibilities will include quantitative investigation of the in vivo hip and knee biomechanics of patients with total joint replacements. The candidate 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 role offers the opportunity to be involved in multiple novel projects enabling the development of a wide range of research experience. Under the supervision of Professor Young-Min Kwon, the candidate will work with a multidisciplinary team of engineers, surgeons, and patients and will develop a strong network with Harvard faculty. The candidate will have the opportunity to present our work nationally and internationally.
Qualified candidates should have a PhD in biomedical, bioengineering, mechanical, or a related engineering discipline and should have graduate research experience with musculoskeletal joint biomechanics. The candidate should have experience in conducting human joint kinematics and kinetics evaluation using imaging and motion analysis techniques. Strong written and oral communication skills are necessary. Proficiency in MATLAB programming is highly desirable.
Interested applicants should submit a CV/resume and contact information for three references (full name, email, and phone). Please submit your application as an email attachment to Melissa Calverley at email@example.com.