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<p><b>Background</b></p>
<p>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. </p>
<p><b>Research question</b></p>
<p>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. </p>
<p><b>Methods</b></p>
<p>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. </p>
<p><b>Results</b></p>
<p>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. </p>
<p><b>Significance</b></p>
<p>This study demonstrates the potential of ML for clinical
assessment of knee disorders in an accurate and reliable manner.</p>
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Application of artificial intelligence in data processing
