Determining the relationship between tibiofemoral geometry and passive motion with partial least squares regression.
Dermot O'RourkeFrancesca BucciWilliam S BurtonRami M A Al-DiriniMark TaylorSaulo MartelliPublished in: Journal of orthopaedic research : official publication of the Orthopaedic Research Society (2023)
Tibiofemoral geometry influences knee passive motion and understanding their relationship can provide insight into knee function and mechanisms of injury. However, the complexity of the geometric constraints has made characterising the relationship challenging. The aim of this study was to determine the tibiofemoral bone geometries that explain the variation in passive motion using a partial least squares regression (PLSR) model. The PLSR model was developed for 29 healthy cadaver specimens (10 female, 19 male) with femur and tibia geometries retrieved from MRI images and 6-DOF tibiofemoral kinematics determined during a flexion cycle with minimal medial pressure. The first 13 PLS components explained 90% of the variation in the kinematics and accounted for 89% of the variation in geometry. The first three PLS components which shared geometric changes to particular surface congruencies of the tibial and femoral condyles explained the most amount of variation in the kinematics, primarily in anterior-posterior translation. Meanwhile, variations in femoral condyle width and the intercondylar space, tibia plateau size and conformity, and tibia eminences heights in PLS 2 and PLS 4 explained the greatest amount of variation in internal-external rotation. PLS 4 exhibiting variation in overall size of the knee accounted for greatest amount of variation in geometry (50%) and had the greatest influence on the abduction-adduction motion and some on internal-external rotation but, overall, explained only a small proportion of the kinematics (10%). Elucidating the complex relationship between tibiofemoral bone geometry and passive kinematics may help personalise treatments for improved functional outcomes in patients. This article is protected by copyright. All rights reserved.
Keyphrases
- total knee arthroplasty
- bone mineral density
- magnetic resonance imaging
- knee osteoarthritis
- end stage renal disease
- newly diagnosed
- computed tomography
- chronic kidney disease
- machine learning
- high resolution
- optical coherence tomography
- prognostic factors
- convolutional neural network
- postmenopausal women
- finite element
- patient reported