Artificial Intelligence for Clinical Gait Diagnostics of Knee
Osteoarthritis: An Evidence - based Review and Analysis
Abstract
Background
Knee osteoarthritis (OA) remains a leading aetiology of disability
worldwide. With recent advances in gait analysis, clinical assessment of
such a knee-related condition has been improved. Although motion capture
(mocap) technology is deemed the gold standard for gait analysis, it
heavily relies on adequate data processing to yield clinically
significant results. Moreover, gait data is non-linear and
high-dimensional. Due to missing data involved in a mocap session and
typical statistical assumptions, conventional data processing methods
are unable to reveal the intrinsic patterns to predict gait
abnormalities.
Research question
Albeit studies have demonstrated the potential of Artificial
Intelligence (AI) algorithms to address these limitations, these
algorithms have not gained wide acceptance amongst biomechanists. The
most common AI algorithms used in gait analysis are based on machine
learning (ML) and artificial neural networks (ANN). By comparing the
predictive capability of such algorithms from published studies, we
assessed their potential to augment current clinical gait diagnostics
when dealing with knee OA.
Methods
Thus, an evidence-based review and analysis were conducted. With over
188 studies identified, 8 studies met the inclusion criteria for a
subsequent analysis, accounting for 78 participants overall.
Results
The classification performance of ML and ANN algorithms was
quantitatively assessed. The test classification accuracy (ACC),
sensitivity (SN), specificity (SP) and area under the curve (AUC) of the
ML-based algorithms were clinically valuable, i.e., all higher than
85%, differently from those obtained via ANN.
Significance
This study demonstrates the potential of ML for clinical assessment of
knee disorders in an accurate and reliable manner.