Using Principal Component Analysis to Identify Differences in Knee Joint Function between a Non-pathological Group and a Pre-high Tibial Osteotomy Cohort

Description

In this presentation, Jake Bowd discusses the use of principal component analysis (PCA) to explore differences in knee joint function between a healthy cohort and individuals awaiting high tibial osteotomy due to medial knee osteoarthritis. This prevalent condition affects millions, leading to the necessity of surgical interventions such as osteotomy, which aims to realign lower limbs and relieve pressure on the knee. Traditional gait analysis methods often overlook important data, making PCA a valuable tool for unbiased data reduction and discerning subtle differences in kinematic patterns.



Bowd outlines the study's objective, which is to quantify variability in knee kinematic waveforms across two distinct groups (28 healthy individuals and 30 awaiting surgery). By utilizing advanced motion capture technologies for data collection, the research aims to retain temporal data typically discarded in conventional analyses. The results reveal significant findings across various planes of knee movement, with PCA accounting for a high percentage of variance, thus providing insights into patient biomechanics that could inform clinical decision-making post-surgery. The presentation concludes with an invitation for questions and a discussion about possible future analyses combining PCA with canonical variance analysis.

DOI: 10.1302/3114-221108

Specialties

Conferences