Artificial Intelligence for Clinical Gait Diagnostics of Knee Osteoarthritis: An Evidence - based Review and Analysis

Abstract

<div> <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> </div>

A Survey of Human Gait-Based Artificial Intelligence Applications

“… 2) studies that partially overlap with our studies, such as the literature review on PD diagnosis by Mei et al (2021) , that in addition to PD diagnosis by gait, also discuss other modalities such as voice, handwriting, magnetic resonance imaging (MRI), etc. 3) studies that are in the scope of this review but only cover a specific topic such as human motion trajectory prediction ( Rudenko et al, 2020 ), wearable sensing technologies for sports biomechanics ( Taborri et al, 2020 ), self-powered sensors and systems ( Wu et al, 2020 ), person re-Identification ( Wang et al, 2016 ), ( Nambiar et al, 2019 ), ( Karanam et al, 2019 ), machine learning in soft robotics ( Kim et al, 2021 ), ambient assisted living technologies (mostly AI-enabled and gait-related) ( Cicirelli et al, 2021 ), human action recognition ( Gurbuz and Amin, 2019 ), biomechanics ( Halilaj et al, 2018 ), gait recognition ( Kusakunniran, 2020 ), ( Singh et al, 2018 ), ( Wan C. et al, 2018 ), gait event detection and gait phase recognition ( Prasanth et al, 2021 ), clinical gait diagnostics of knee osteoarthritis ( Parisi et al, 2020 ), knee pathology assessment ( Abid et al, 2019 ), data preprocessing in gait classification ( Burdack et al, 2019 ), age estimation ( Aderinola et al, 2021 ), and banchamrk datasets ( Nunes et al, 2019 ). A survey paper by Alzubaidi et al (2021) provides an overview of deep learning, with helpful definitions and a discussion of strengths, limitations, and future trends of various deep learning techniques.…”

A LabVIEW Interface for RehaStim 2

“…Furthermore, it can automate calibration routines to optimize electrode configuration and stimulation parameters to elicit a target response [8]. Implementing custom algorithms for parametric optimization can improve overall stimulation [9]- [11]. For this purpose, the source files have been made openly available.…”