Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Radiology and Biomedical Imaging in Musculoskeletal Research
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Radiology and Biomedical Imaging in Musculoskeletal Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1453

Special Issue Editors

Dr. Vlad Vunvulea

E-Mail Website
Guest Editor
Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
Interests: stroke; endovascular; interventional radiology
Dr. Ioana Hǎlmaciu

E-Mail Website
Guest Editor
Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
Interests: inflammatory biomarkers

Special Issue Information

Dear Colleagues,

Radiology and biomedical imaging play a critical role in advancing our understanding of musculoskeletal research. Musculoskeletal disorders, injuries, and diseases are among the most prevalent causes of disability worldwide, affecting millions of people and placing a significant burden on healthcare systems. Accurate diagnosis, effective treatment, and comprehensive rehabilitation rely heavily on imaging technologies such as MRI, CT, and ultrasound, which allow for detailed visualization of musculoskeletal structures and pathology.

This Special Issue, “Radiology and Biomedical Imaging in Musculoskeletal Research”, aims to explore cutting-edge advancements in the use of imaging modalities for enhancing musculoskeletal diagnosis, treatment planning, and rehabilitation. We invite submissions that cover a wide range of topics, including but not limited to the following:

  • Advanced imaging techniques in the diagnosis of musculoskeletal injuries and diseases;
  • The role of imaging in monitoring treatment efficacy and recovery outcomes;
  • Innovations in interventional radiology for musculoskeletal disorders;
  • Artificial intelligence and machine learning applications in musculoskeletal imaging;
  • Biomechanical imaging and its contribution to understanding musculoskeletal function;
  • Comparative studies on the effectiveness of different imaging modalities.

We encourage researchers, radiologists, and clinicians from diverse fields including radiology, orthopedics, sports medicine, and biomedical engineering to submit their original research, review articles, and meta-analyses for this Special Issue. Through this collaboration, we aim to foster new insights into musculoskeletal imaging and improve clinical outcomes for patients.

Dr. Vlad Vunvulea
Dr. Ioana Hǎlmaciu
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • radiology
  • biomedical imaging, such as MRI, CT, or ultrasound
  • musculoskeletal
  • biomechanical imaging
  • orthopedics
  • sports medicine

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 3136 KiB  
Article
Effect of Contralateral Cervical Glide on the Suprascapular Nerve: An In Vitro and In Vivo Study
by Marta Montané-Blanchart, Maribel Miguel-Pérez, Lourdes Rodero-de-Lamo, Pasqual Navarro-Cano and Albert Pérez-Bellmunt
Appl. Sci. 2025, 15(13), 6987; https://doi.org/10.3390/app15136987 - 20 Jun 2025
Viewed by 227
Abstract
Background: Suprascapular neuropathy is a known cause of shoulder pain. Although neurodynamic techniques are widely used to treat peripheral neuropathies, the mechanical behavior of the suprascapular nerve in the shoulder region remains poorly understood. Objectives: This study aimed to analyze the [...] Read more.
Background: Suprascapular neuropathy is a known cause of shoulder pain. Although neurodynamic techniques are widely used to treat peripheral neuropathies, the mechanical behavior of the suprascapular nerve in the shoulder region remains poorly understood. Objectives: This study aimed to analyze the mechanical behavior of the suprascapular nerve during a contralateral cervical glide and an infraspinatus muscle contraction. Methods: The study was conducted in two phases. First, nerve movement was analyzed in 12 cryopreserved cadaveric shoulders using anatomical dissection. Second, suprascapular nerve displacement was assessed in 34 shoulders from 17 healthy volunteers using ultrasound imaging. Results: In cadaveric dissections, the contralateral cervical glide produced a proximal nerve displacement of 1.85 mm at the suprascapular notch. In the ultrasound study, this maneuver resulted in horizontal and vertical displacements of 1.18 mm and 0.39 mm, respectively. In contrast, infraspinatus muscle contraction caused a distal displacement of 3.21 mm in the cadaveric study, and ultrasound imaging showed horizontal and vertical displacements of 1.34 mm and 0.75 mm, respectively. All reported displacements were statistically significant (p < 0.05). Conclusions: The findings of both phases of the study contribute to a better understanding of suprascapular nerve biomechanics and may inform clinical neurodynamic interventions. Full article
(This article belongs to the Special Issue Radiology and Biomedical Imaging in Musculoskeletal Research)
►▼ Show Figures

Figure 1

Review

Jump to: Research

19 pages, 742 KiB  
Review
Artificial Intelligence-Based Models for Automated Bone Age Assessment from Posteroanterior Wrist X-Rays: A Systematic Review
by Isidro Miguel Martín Pérez, Sofia Bourhim and Sebastián Eustaquio Martín Pérez
Appl. Sci. 2025, 15(11), 5978; https://doi.org/10.3390/app15115978 - 26 May 2025
Viewed by 914
Abstract
Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated [...] Read more.
Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle–Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., BoneXpert®, Physis®, VUNO Med®-BoneAge) achieved mean absolute errors of 2–31.8 months—significantly surpassing Greulich–Pyle and Tanner–Whitehouse benchmarks—and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards. Full article
(This article belongs to the Special Issue Radiology and Biomedical Imaging in Musculoskeletal Research)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop