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Muscle Function and Neuromuscular Disorders: AI and Biomechanics in Diagnosis...
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Muscle Function and Neuromuscular Disorders: AI and Biomechanics in Diagnosis and Rehabilitation

A special issue of Muscles (ISSN 2813-0413).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1442

Special Issue Editors

Prof. Dr. Ka-Chun (Joseph) Siu

E-Mail Website
Guest Editor
Department of Health and Rehabilitations Sciences, University of Nebraska Medical Center, Omaha, NE 68198-4420, USA
Interests: human movement; gait biomechanics; motor learning; rehabilitation; biomechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Saiteja Malisetty

E-Mail Website
Guest Editor
College of IS&T, University of Nebraska at Omaha, 6001 Dodge Street, 172 PKI, Omaha, NE 68182-0116, USA
Interests: machine learning; data science; healthcare AI; neuromuscular disorders; biomechanics

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence (AI) and biomechanics are opening new possibilities for the study and treatment of neuromuscular disorders that impact muscle health, function, and repair. This Special Issue, “Muscle Function and Neuromuscular Disorders: AI and Biomechanics in Diagnosis and Rehabilitation”, focuses on the use of AI, machine learning, and biomechanical analysis to address a variety of muscle-related pathophysiologies. By integrating computational techniques, researchers and clinicians can gain new insights into muscle degeneration, function regulation, and personalized rehabilitation strategies, which are essential for maintaining and restoring muscle health.

This Special Issue aims to publish original research and review articles exploring the impact of neuromuscular disorders on muscle function and repair. Topics of interest include AI-driven muscle imaging analysis, predictive modeling for muscle degeneration, and biomechanical approaches for improved rehabilitation outcomes.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • AI-based analyses of muscle function and degeneration in neuromuscular disorders;
  • The biomechanical modeling of muscle movement and gait for diagnostic and therapeutic purposes;
  • Machine learning models predicting muscle repair and recovery in disorders such as muscular dystrophy and myopathies;
  • Real-time AI feedback systems for muscle control and motor learning in rehabilitation settings;
  • Applications of AI in wearable technology for monitoring muscle health and function;
  • AI integration in multi-modal data analysis to support personalized muscle health interventions.

By aligning AI and biomechanics with muscle health research, this Special Issue provides a platform for interdisciplinary studies focused on neuromuscular function, rehabilitation, and muscle pathology. This issue will benefit researchers and clinicians who seek innovative approaches to improve muscle health and the management of muscle-related diseases.

Prof. Dr. Ka-Chun (Joseph) Siu
Dr. Saiteja Malisetty
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. Muscles is an international peer-reviewed open access quarterly 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 1000 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

  • muscle function
  • neuromuscular disorders
  • biomechanics
  • AI in muscle imaging
  • motor learning
  • rehabilitation in muscle health
  • predictive modeling for muscle repair
  • personalized muscle health interventions
  • muscle degeneration monitoring
  • wearable technology for muscle health

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

15 pages, 1553 KB  
Article
Hamstring Strain Injury Risk in Soccer: An Exploratory, Hypothesis-Generating Prediction Model
by Afxentios Kekelekis, Rabiu Muazu Musa, Pantelis T. Nikolaidis, Filipe Manuel Clemente and Eleftherios Kellis
Muscles 2025, 4(4), 50; https://doi.org/10.3390/muscles4040050 - 4 Nov 2025
Viewed by 414
Abstract
Hamstring strain injuries (HSI) are common in soccer and remain challenging to predict, as traditional risk factors often fail to capture the multifactorial nature of injury susceptibility. This prospective cohort study aimed to develop and internally validate a machine learning-assisted logistic regression model [...] Read more.
Hamstring strain injuries (HSI) are common in soccer and remain challenging to predict, as traditional risk factors often fail to capture the multifactorial nature of injury susceptibility. This prospective cohort study aimed to develop and internally validate a machine learning-assisted logistic regression model for predicting hamstring injuries in amateur soccer players using preseason clinical and strength-related variables. A total of 120 male players were followed for one competitive season (30 weeks). Baseline predictors included age, body mass index, previous injury, and bilateral isometric hip and knee strength measured via handheld dynamometry. Twenty initial predictors were reduced to ten through symmetrical uncertainty feature ranking before training a logistic regression model with elastic-net regularization (training set: n = 83; test set: n = 37) using nested four-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration metrics, and confusion matrices. During follow-up, 21 players sustained at least one HSI (32 events; 28% reinjuries), yielding an events-per-variable ratio of 2.1, below ideal thresholds and suggesting possible overfitting. On the independent test set, the model achieved an accuracy of 64.9%, AUC of 0.68 (95% CI 0.52–0.84), calibration slope of 0.85, and intercept of −0.12, with a sensitivity of 60% and specificity of 65.6%. Dominant-leg hip abduction strength was the only statistically significant predictor (OR = 0.82, 95% CI 0.70–0.96), while permutation importance analyses identified previous hamstring injury as the most stable contributor to model performance. Neither age nor hamstring isometric strength demonstrated predictive value. Although model discrimination was moderate and calibration indicated mild overfitting, findings reinforce the prognostic relevance of prior injury and suggest that reduced hip abduction strength may serve as an emerging candidate marker. This study, classified as a TRIPOD Category 2 model (development without external validation), provides preliminary, hypothesis-generating evidence supporting the use of multivariate strength and history-based predictors in future, larger-scale injury prediction research. Full article
(This article belongs to the Special Issue Muscle Function and Neuromuscular Disorders: AI and Biomechanics in Diagnosis and Rehabilitation)
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