BioMed Research International

Application of Artificial Intelligence and Machine Learning in Musculoskeletal Biomechanics and Data Delving

Publishing date
01 Oct 2022
Submission deadline
27 May 2022

Lead Editor

1Ningbo University, Ningbo, China

2Hong Kong Baptist University, Hong Kong

3University of Szeged, Szeged, Hungary

4Cardiff Metropolitan University , Cardiff, UK

This issue is now closed for submissions.

Application of Artificial Intelligence and Machine Learning in Musculoskeletal Biomechanics and Data Delving

This issue is now closed for submissions.


The human musculoskeletal system (also known as the human locomotor system) refers to the system having its muscles attached to an internal skeletal system, and is necessary for humans to perform physical activities. This system describes how bones are connected to other bones and muscle fibers via connective tissue such as tendons and ligaments. Muscles keep bones in place and also play a role in the movement of bones.

There are, however, diseases and disorders that may adversely affect the function and overall effectiveness of the system. These diseases can be difficult to diagnose due to the close relation of the musculoskeletal system to other internal systems. Risk factors for lower extremity musculoskeletal injury risk include over-training, inadequate nutrition, previous injury, gender, limb dominance, ankle and knee joint laxity, muscle strength, imbalance, and postural stability. Meanwhile, the identification of risk factors of lower extremity musculoskeletal injuries has been challenging because of the lack of consensus on risk factors. Artificial intelligence is a field of mathematical engineering which has the potential to enhance healthcare through new care delivery strategies, informed decision making, and facilitation of patient engagement. Machine learning is a form of narrow artificial intelligence that can be used to automate decision-making and make predictions based upon patient data. Machine learning models have previously been developed to explore risk factors in specific populations.

This Special Issue welcomes original research and review articles and aims to provide a multidisciplinary discussion forum, covering all bioengineering professions, regarding the role of computer science to predict the risk of musculoskeletal injury.

Potential topics include but are not limited to the following:

  • Applications of artificial intelligence and machine learning in musculoskeletal physiotherapy
  • Development of machine learning methods to support personalized neuromusculoskeletal modeling
  • Employing machine learning to predict lower extremity injury risk factors in specific populations
  • Ergonomic risk assessment based on computer vision and machine learning
  • Solving musculoskeletal biomechanics with machine learning methods
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