Kevin B. Shelburne
Papers
5
Total Citations
30
H-Index
3
About
Kevin B. Shelburne is a leading researcher in computational orthopedics and biomechanics, specializing in the development and validation of subject-specific knee models. His work bridges the gap between in vivo imaging and computational simulation, aiming to create accurate digital twins of the living knee for improved surgical planning and outcome prediction. A major contribution is the design of an apparatus for in vivo knee laxity assessment using high-speed stereo radiography, enabling unprecedented measurement of knee motion under dynamic loading. Shelburne’s research has demonstrated how implant placement parameters in robotic-assisted total knee arthroplasty directly influence post-operative varus and valgus angles, and he has identified key determinants of pivot kinematics in posterior stabilized knee replacements. His studies on validating subject-specific models from in vivo measurements—cited over 30 times collectively—address a critical limitation in the field: the historical reliance on cadaveric data. By pioneering methods to calibrate computational models using living subject data, Shelburne is advancing the path toward personalized orthopedic care and more reliable predictive tools for surgeons and patients.
Research Focus
Key Achievements
Top Papers
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- 4Validating subject-specific knee models from in vivo measurements3 citations · 2025
- 5Validation of Subject-Specific Knee Models from In Vivo Measurements2 citations · 2024