Open AccessComputational framework for population-based evaluation of TKR-implanted patellofemoral joint mechanics
Author(s) -
Azhar A. Ali,
Chadd W. Clary,
Lowell M. Smoger,
Douglas A. Dennis,
Clare K. Fitzpatrick,
Paul J. Rullkoetter,
Peter J. Laz
Publication year - 2020
Publication title -
biomechanics and modeling in mechanobiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 68
eISSN - 1617-7959
pISSN - 1617-7940
DOI - 10.1007/s10237-020-01295-7
Subject(s) - latin hypercube sampling , kinematics , population , patella , finite element method , computer science , sampling (signal processing) , biomechanics , monte carlo method , implant , knee joint , orthodontics , biomedical engineering , mathematics , structural engineering , engineering , surgery , physics , statistics , anatomy , medicine , computer vision , environmental health , filter (signal processing) , classical mechanics
Differences in patient anatomy are known to influence joint mechanics. Accordingly, intersubject anatomical variation is an important consideration when assessing the design of joint replacement implants. The objective of this study was to develop a computational workflow to perform population-based evaluations of total knee replacement implant mechanics considering variation in patient anatomy and to assess the potential for an efficient sampling strategy to support design phase screening analyses. The approach generated virtual subject anatomies using a statistical shape model of the knee and performed virtual implantation to size and align the implants. A finite-element analysis simulated a deep knee bend activity and predicted patellofemoral (PF) mechanics. The study predicted bounds of performance for kinematics and contact mechanics and investigated relationships between patient factors and outputs. For example, the patella was less flexed throughout the deep knee bend activity for patients with an alta patellar alignment. The results also showed the PF range of motions in AP and ML were generally larger with increasing femoral component size. Comparison of the 10-90% bounds between sampling strategies agreed reasonably, suggesting that Latin Hypercube sampling can be used for initial screening evaluations and followed up by more intensive Monte Carlo simulation for refined designs. The platform demonstrated a functional workflow to consider variation in joint anatomy to support robust implant design.