What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research.
Abstract: Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported investigations into bone fatigue, subchondral bone damage in the joints of both humans and animals, and identification of vital parameters responsible for retaining integrity of anatomical regions during normal activities in all species are heavily reliant on equine biomechanical research. Horse racing is a lucrative industry and injury prevention in expensive thoroughbreds has encouraged the implementation of various measurement techniques, which results in massive data generation. ML substantially accelerates analysis and interpretation of data and provides considerable advantages over traditional statistical tools historically adopted in biomechanical research. This paper provides the reader with: a brief introduction to AI, taxonomy and several types of ML algorithms, working principle of a feedforward artificial neural network (ANN), and, a detailed review of the applications of AI, ML, and ANN in equine biomechanical research (i.e. locomotory system function, gait analysis, joint and bone mechanics, and hoof function). Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations, avoid and/or minimize injuries, and encourage early detection of such injuries in the first place.
Copyright © 2021 Elsevier Ltd. All rights reserved.
Publication Date: 2021-08-12 PubMed ID: 34412024DOI: 10.1016/j.jmbbm.2021.104728Google Scholar: Lookup
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Summary
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The research article discusses how artificial intelligence and machine learning can aid in equine biomechanical research, which further helps to prevent injuries in horses as well as lending insights into human tissue and structure deformations. The research particularly concentrates on AI’s contribution to horse biomechanics to improve clinical assessments, predict and mitigate injuries, and enable real-time simulations.
Introduction to AI and Machine Learning
- The article begins with a quick overview of artificial intelligence (AI) and machine learning (ML). These are scientific fields where machines are trained to mimic human intelligence.
- It also explains that machines can learn from existing data and use this accumulated knowledge to complete complex tasks such as regression analysis, pattern recognition, and predicting future outcomes.
Importance of Equine Biomechanical Research
- The paper then delves into the significance of equine biomechanical research, the study of the mechanical aspects of horses’ movements and structures.
- Equine biomechanical models are used to examine the effects of mechanical loading and induced deformations on tissues and structures in animals and humans. These studies are vital for understanding tissue resilience and identifying mechanisms that can help prevent injuries.
- This research plays a crucial role in the horse racing industry, which, in endeavours to prevent injuries in thoroughbreds, generates large amounts of data.
Machine Learning in Biomechanical Research
- Machine learning is identified as a valuable tool for managing, analyzing, and interpreting this data, offering substantial advantages over traditional statistical methods used in biomechanical research.
- A detailed review of the applications of AI, ML, and artificial neural networks (ANN) in equine biomechanical research is included. These tools are employed for understanding locomotory system function, gait analysis, joint and bone mechanics, and hoof function, among other things.
Potential Applications and Improvements
- The paper further highlights that the wider application of these data-driven tools can improve clinical assessments by enabling real-time simulations.
- Such tools suggest a potential to predict and decrease injurious incidences or encourage early detection of injuries.
Cite This Article
APA
Mouloodi S, Rahmanpanah H, Gohari S, Burvill C, Tse KM, Davies HMS.
(2021).
What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research.
J Mech Behav Biomed Mater, 123, 104728.
https://doi.org/10.1016/j.jmbbm.2021.104728 Publication
Researcher Affiliations
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia. Electronic address: saeed.mouloodi@unimelb.edu.au.
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
- Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia. Electronic address: ktse@swin.edu.au.
- Department of Veterinary Biosciences, The University of Melbourne, Melbourne, Australia.
MeSH Terms
- Algorithms
- Animals
- Artificial Intelligence
- Horses
- Machine Learning
- Neural Networks, Computer
Citations
This article has been cited 2 times.- Parmentier JIM, Bosch S, van der Zwaag BJ, Weishaupt MA, Gmel AI, Havinga PJM, van Weeren PR, Braganca FMS. Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks.. Sci Rep 2023 Jan 13;13(1):740.
- Alexander N, Brunner R, Cip J, Viehweger E, De Pieri E. Increased Femoral Anteversion Does Not Lead to Increased Joint Forces During Gait in a Cohort of Adolescent Patients.. Front Bioeng Biotechnol 2022;10:914990.
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