A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects
Document Type
Article
Publication Date
1-1-2024
Abstract
Background: Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70 of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Methods: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Findings: Most studies (83.1) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100 in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64), while artificial neural network demonstrated the highest accuracy in eight studies. Interpretations: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. © 2024 Elsevier Ltd
Keywords
Biomechanics, Kinematics, Learning algorithms, Neural networks, Patient monitoring, Support vector machines, Wearable sensors, 'current, Ankle, Ankle biomechanics, Electronic database, Evidence-based, Future prospects, Machine-learning, Rehabilitation strategy, Scoping review, State of the art, Kinetics
Divisions
infosystem,biomedengine,sportmedicine
Funders
Universiti Malaya (ST035- 2021)
Publication Title
Clinical Biomechanics
Volume
113
Publisher
Elsevier