<p><b>Background</b></p>
<p>Knee osteoarthritis
(OA) remains a leading aetiology of disability worldwide. Clinical assessment
of such knee-related conditions has improved with recent advances in gait
analysis. Despite being a gold standard method, gait data acquired by motion capture
(mocap) technology are highly non-linear and dimensional, which make
traditional gait analysis challenging. Thus, extrinsic algorithms need to be
used to make sense of gait data. Supervised Machine Learning (ML)-based
classifiers outperform conventional statistical methods in revealing intrinsic
patterns that can discern gait abnormalities when using mocap data, making them
a suitable tool for aiding diagnosis of knee OA.</p>
<p><b>Research question</b></p>
<p>Studies have demonstrated
the accuracy of supervised ML-based classifiers in gait analysis. However, these
techniques have not gained wide acceptance amongst biomechanists for two
reasons: the reliability of such methods has not been assessed and there is no
consensus on which classifier or group of classifiers to select. Specifically,
it is not clear whether classifiers that leverage optimal separating
hyperplanes (OSH) or artificial neural networks (ANN) are more accurate and
reliable.</p>
<p><b>Methods</b></p>
<p>A systematic review
and meta-analysis were conducted to assess the capability of such algorithms to
predict pathological kinematic and kinetic gait patterns as indicators of knee
OA. With 153 eligible studies, 6 studies met the
inclusion criteria for a subsequent meta-analysis, accounting for <a>273 healthy subjects and 313 patients </a>with symptomatic
knee OA. The classification performance of supervised ML classifiers (OSH- or
ANN-based) used in these studies was quantitatively assessed and compared across
four following performance metrics: classification accuracy on the test set
(ACC), sensitivity (SN), specificity (SP), and area under the receiver
operating characteristic curve (AUC). </p>
<p><b>Results</b></p>
<p>There was no
statistically significant discrepancy in the ACC between OSH- and ANN-based
classifiers when dealing with kinetic and kinematic data concurrently, as well
as when considering only kinematic data. However, there was a statistically
significant difference in their SN and SP, with the ANN-based classifiers
having higher SN and SP than OSH-based algorithms. As only one of the eligible studies reported AUC,
this metric could not be assessed statistically across studies.</p>
<p><b>Significance</b></p>
<p>This study supports
the use of ANN-based algorithms for classifying knee OA-related gait patterns as
having a higher sensitivity and specificity than OSH-based classifiers. Considering
their higher reliability, leveraging supervised ANN-based methods can aid biomechanists
to diagnose knee OA objectively.</p>
Relationship between Kellgren-Lawrence score and 3D kinematic gait analysis of patients with medial knee osteoarthritis using a new gait system
