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Application of Biomechanical and Artificial Intelligence Tools in Prevention and Diagnosis of Musculoskeletal Impairment

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 9289

Special Issue Editors

Dr. Themistoklis Tsatalas

E-Mail Website
Guest Editor
Department of Physical Education and Sport Science, University of Thessaly, 421 00 Trikala, Greece
Interests: sports and clinical biomechanics; ACL injury; exercise-induced muscle damage; gait analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Katsavelis

E-Mail Website
Guest Editor
Department of Exercise Science and Pre-Health Profession, Creighton University, Omaha, NE, USA
Interests: human movement; lower extremity injuries; real-time biofeedback
Dr. Christos Kokkotis

E-Mail Website
Guest Editor
Department of Physical Education and Sport Science, Democritus University of Thrace, Komotini, Greece
Interests: machine learning; statistics; biomechanics; knee osteoarthritis

Special Issue Information

Dear Colleagues,

Musculoskeletal impairment, whether as a result of injury, disability, or disease, affects millions of people worldwide and imposes an ever-increasing economic burden on national healthcare systems. Technological advancements necessitate the use of biomechanical and artificial intelligence (AI) tools for the prevention and diagnosis of sport injuries or pathologies.

This Special Issue will focus on the prevention and diagnosis of musculoskeletal impairment by utilizing advanced biomechanical tools and AI applications. Authors are kindly asked to submit original manuscripts or reviews related to either the prevention or diagnosis of musculoskeletal impairment by applying biomechanical tools, AI, or a combination of the two.

Papers can include but are not limited to any kind of upper or lower limb injury or pathology (i.e., total knee or hip arthroplasty, ligament tears or reconstructions, ankle sprain, running related injuries, exercise-induced muscle damage, osteoarthritis, osteoporosis, stroke, myopathies, etc.). Under the umbrella of AI can be included machine learning (ML) and other types of data analytics techniques that, either standalone or combined with advanced biomechanical tools, can provide comprehensive solutions for the prevention and diagnosis of musculoskeletal impairment. 

We look forward to receiving your contributions.

Dr. Themistoklis Tsatalas
Prof. Dr. Dimitrios Katsavelis
Dr. Christos Kokkotis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomechanics
  • artificial intelligence
  • machine learning
  • public health
  • musculoskeletal injuries
  • osteoarthritis
  • anterior cruciate ligament
  • prevention, diagnostic tools
  • risk factors

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Published Papers (3 papers)

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17 pages, 1049 KiB  
Article
Epidemiological Profile among Greek CrossFit Practitioners
by Konstantinos Vassis, Athanasios Siouras, Nikolaos Kourkoulis, Ioannis A. Poulis, Georgios Meletiou, Anna-Maria Iliopoulou and Ioannis Misiris
Int. J. Environ. Res. Public Health 2023, 20(3), 2538; https://doi.org/10.3390/ijerph20032538 - 31 Jan 2023
Cited by 1 | Viewed by 2469
Abstract
CrossFit (CF) is a popular and rapidly expanding training program in Greece and worldwide. However, there is a lack of scientific evidence on the risk of musculoskeletal injuries related to CF in the Greek population. A self-administered survey of 1224 Greek CF practitioners [...] Read more.
CrossFit (CF) is a popular and rapidly expanding training program in Greece and worldwide. However, there is a lack of scientific evidence on the risk of musculoskeletal injuries related to CF in the Greek population. A self-administered survey of 1224 Greek CF practitioners aged 18 to 59 was conducted and analyzed using the Statistical Package for Social Sciences (SPSS) software. The highest percentage of the participants (34%) practiced 5 days per week for 60 min (42.2%) and had 2 days per week of rest (41.7%). A total of 273 individuals (23%) participated in CF competitions and 948 (77%) did not. The results showed that the most common injuries were muscle injuries (51.3%), followed by tendinopathies (49.6%) and joint injuries (26.6%). The shoulders (56.6%; n = 303), knees (31.8%; n = 170), and lumbar spine (33.1%; n = 177) were the most commonly injured locations. The logistic regression model showed that participation in competitions (p = 0.001), rest per week (p = 0.01), duration of training per session (p = 0.001), and frequency of training per week (p = 0.03) were statistically significant factors for injury. Training level was not a statistically significant factor for injury (p = 0.43). As CF continues to gain popularity on a global scale and the number of athletes gradually increases, it is important to monitor the safety of practitioners. Clinicians should consider participation in competitions, rest, training duration, and frequency in order to make CF safer. Full article
(This article belongs to the Special Issue Application of Biomechanical and Artificial Intelligence Tools in Prevention and Diagnosis of Musculoskeletal Impairment)
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12 pages, 989 KiB  
Systematic Review
Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review
by Christos Kokkotis, Georgios Chalatsis, Serafeim Moustakidis, Athanasios Siouras, Vasileios Mitrousias, Dimitrios Tsaopoulos, Dimitrios Patikas, Nikolaos Aggelousis, Michael Hantes, Giannis Giakas, Dimitrios Katsavelis and Themistoklis Tsatalas
Int. J. Environ. Res. Public Health 2023, 20(1), 448; https://doi.org/10.3390/ijerph20010448 - 27 Dec 2022
Cited by 1 | Viewed by 2471
Abstract
Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the [...] Read more.
Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients. Full article
(This article belongs to the Special Issue Application of Biomechanical and Artificial Intelligence Tools in Prevention and Diagnosis of Musculoskeletal Impairment)
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18 pages, 873 KiB  
Systematic Review
The Effects of Action Observation Therapy as a Rehabilitation Tool in Parkinson’s Disease Patients: A Systematic Review
by Ioannis Giannakopoulos, Panagiota Karanika, Charalambos Papaxanthis and Panagiotis Tsaklis
Int. J. Environ. Res. Public Health 2022, 19(6), 3311; https://doi.org/10.3390/ijerph19063311 - 11 Mar 2022
Cited by 6 | Viewed by 3060
Abstract
During Action Observation (AO), patients observe human movements that they then try to imitate physically. Until now, few studies have investigated the effectiveness of it in Parkinson’s disease (PD). However, due to the diversity of interventions, it is unclear how the dose and [...] Read more.
During Action Observation (AO), patients observe human movements that they then try to imitate physically. Until now, few studies have investigated the effectiveness of it in Parkinson’s disease (PD). However, due to the diversity of interventions, it is unclear how the dose and characteristics can affect its efficiency. We investigated the AO protocols used in PD, by discussing the intervention features and the outcome measures in relation to their efficacy. A search was conducted through MEDLINE, Scopus, Cochrane, and WoS until November 2021, for RCTs with AO interventions. Participant’s characteristics, treatment features, outcome measures, and main results were extracted from each study. Results were gathered into a quantitative synthesis (MD and 95% CI) for each time point. Seven studies were included in the review, with 227 participants and a mean PEDro score of 6.7. These studies reported positive effects of AO in PD patients, mainly on walking ability and typical motor signs of PD like freezing of gait. However, disagreements among authors exist, mainly due to the heterogeneity of the intervention features. In overall, AO improves functional abilities and motor control in PD patients, with the intervention dose and the characteristics of the stimulus playing a decisive role in its efficacy. Full article
(This article belongs to the Special Issue Application of Biomechanical and Artificial Intelligence Tools in Prevention and Diagnosis of Musculoskeletal Impairment)
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