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Diagnostics
Special Issues
Advanced Diagnosis and Management of Chronic Musculoskeletal Disorders
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Advanced Diagnosis and Management of Chronic Musculoskeletal Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1676

Special Issue Editor

Dr. Jin Hyuck Lee

E-Mail Website
Guest Editor
Department of Sports Medical Center, Korea University College of Medicine, Seoul 02841, Republic of Korea
Interests: sports injury; rotator cuff injury; shoulder instability; scapular dyskinesis; ACL injury; meniscus tear; patellofemoral pain; ankle instability; foot pain; low back pain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue addresses the topic of the advanced diagnosis and management of chronic musculoskeletal disorders. Therapeutic exercise is known to be an important part of pain management and functional improvement in chronic musculoskeletal diseases. However, because of the wide range of strategies for the diagnosis and management of chronic musculoskeletal diseases, clinicians and physicians must understand and utilize all of these to provide high-quality medical services. Therefore, research that can benefit patients through new and diverse evidence-based advanced diagnostic and treatment methods is important. In this Special Issue of Diagnostics, we invite studies reporting on the advanced diagnosis and management of chronic musculoskeletal disorders in various fields such as orthopedics, rehabilitation medicine, physical therapy, and sports medicine.

Dr. Jin Hyuck Lee
Guest Editor

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. Diagnostics 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 2600 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

  • rotator cuff and shoulder instability
  • knee ligament rupture and osteoarthritis
  • foot and ankle injury
  • cervical and back pain
  • sports medicine

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

Published Papers (2 papers)

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Research

25 pages, 4658 KiB  
Article
AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles
by Mehdi Shirzadi, Mónica Rojas Martínez, Joan Francesc Alonso, Leidy Yanet Serna, Joaquim Chaler, Miguel Angel Mañanas and Hamid Reza Marateb
Diagnostics 2024, 14(20), 2255; https://doi.org/10.3390/diagnostics14202255 - 10 Oct 2024
Viewed by 477
Abstract
Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls [...] Read more.
Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders. Full article
(This article belongs to the Special Issue Advanced Diagnosis and Management of Chronic Musculoskeletal Disorders)
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13 pages, 480 KiB  
Article
Environmental and Genetic Risk Factors in Developmental Dysplasia of the Hip for Early Detection of the Affected Population
by Judit A. Ramírez-Rosete, Alonso Hurtado-Vazquez, Antonio Miranda-Duarte, Sergio Peralta-Cruz, Ramiro Cuevas-Olivo, José Antonio Martínez-Junco, Rosalba Sevilla-Montoya, Berenice Rivera-Paredez, Rafael Velázquez-Cruz, Margarita Valdes-Flores, Claudia Rangel-Escareno, Gerardo J. Alanis-Funes, Laura Abad-Azpetia, Sacnicte G. Grimaldo-Galeana, Monica G. Santamaría-Olmedo and Alberto Hidalgo-Bravo
Diagnostics 2024, 14(9), 898; https://doi.org/10.3390/diagnostics14090898 - 25 Apr 2024
Viewed by 891
Abstract
Diagnosis of developmental dysplasia of the hip (DDH) mostly relies on physical examination and ultrasound, and both methods are operator-dependent. Late detection can lead to complications in young adults. Current evidence supports the involvement of environmental and genetic factors, such as single nucleotide [...] Read more.
Diagnosis of developmental dysplasia of the hip (DDH) mostly relies on physical examination and ultrasound, and both methods are operator-dependent. Late detection can lead to complications in young adults. Current evidence supports the involvement of environmental and genetic factors, such as single nucleotide variants (SNVs). Incorporating genetic factors into diagnostic methods would be useful for implementing early detection and management of affected individuals. Our aim was to analyze environmental factors and SNVs in DDH patients. We included 287 DDH cases and 284 controls. Logistic regression demonstrated an association for sex (OR 9.85, 95% CI 5.55–17.46, p = 0.0001), family history (OR 2.4, 95% CI 1.2–4.5, p = 0.006), fetal presentation (OR 3.19, 95% CI 1.55–6.54, p = 0.002), and oligohydramnios (OR 2.74, 95%CI 1.12–6.70, p = 0.026). A model predicting the risk of DDH including these variables showed sensitivity, specificity, PPV, and NPV of 0.91, 0.53, 0.74, and 0.80 respectively. The SNV rs1800470 in TGFB1 showed an association when adjusted for covariables, OR 0.49 (95% CI 0.27–0.90), p = 0.02. When rs1800470 was included in the equation, sensitivity, specificity, PPV and NPV were 0.90, 0.61, 0.84, and 0.73, respectively. Incorporating no-operator dependent variables and SNVs in detection methods could be useful for establishing uniform clinical guidelines and optimizing health resources. Full article
(This article belongs to the Special Issue Advanced Diagnosis and Management of Chronic Musculoskeletal Disorders)
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