AUTHOR=Cerveri Pietro , Belfatto Antonella , Manzotti Alfonso 

TITLE=Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2020.00253

DOI=10.3389/fbioe.2020.00253

ISSN=2296-4185

ABSTRACT=Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread into a set of matching surfaces by means of compact descriptive quantities, traditionally called ‘modes of variation’ (MoVs). SSMs of bony surfaces have been proposed in biomechanics and or- thopaedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate about the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using ninety-nine bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate as all greater than 0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p=0.02) in correspondence of IER and MoV #5 in correspondence of HKA (p=0.0001), FVV (p=0.001) and TS (p=0.02). We can argue that SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict the potential misalignment in the knee for a new patient by processing the bone shapes removing the need of measuring clinical landmarks as the rotation centers and mechanical axes.