Teoh, Yun Xin and Alwan, Jwan K. and Shah, Darshan S. and Teh, Ying Wah and Goh, Siew Li (2024) A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects. Clinical Biomechanics, 113. p. 106188. ISSN 0268-0033, DOI https://doi.org/10.1016/j.clinbiomech.2024.106188.
Full text not available from this repository.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
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (ST035- 2021) |
| Uncontrolled 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 |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Information System Faculty of Engineering > Biomedical Engineering Department Faculty of Medicine > Sport Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 15 Nov 2024 08:04 |
| Last Modified: | 15 Nov 2024 08:04 |
| URI: | http://eprints.um.edu.my/id/eprint/44767 |
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