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Medicina
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New Strategies for the Diagnosis and Treatment of Rheumatic and...
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New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Hematology and Immunology".

Deadline for manuscript submissions: closed (15 December 2025) | Viewed by 14450

Special Issue Editors

Dr. Elena Amaricai

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Guest Editor
Department of Rehabilitation, Physical Medicine and Rheumatology, Research Center for Assessment of Human Motion, Functionality and Disability, “Victor Babes” University of Medicine and Pharmacy of Timisoara, 300041 Timisoara, Romania
Interests: pediatric rehabilitation; scoliosis; plantar pressure assessment; stabilometry; functional capacity assessment; quality of life
Special Issues, Collections and Topics in MDPI journals
Dr. Carmen Delia Nistor-Cseppento

E-Mail Website
Guest Editor
1. Department of Psycho-Neuroscience and Recovery, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania
2. Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania
Interests: rehabilitation in inflammatory and degenerative diseases; neurological rehabilitation; cardiovascular rehabilitation; respiratory rehabilitation; post-traumatic rehabilitation; mobile health technologies (mHealth)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rheumatic and musculoskeletal diseases represent a growing concern for healthcare systems worldwide, particularly in the context of an aging population and the evolution of diagnostic and treatment modalities. Addressing this challenge requires a multifaceted approach, including increased investment in healthcare resources, education for healthcare professionals, and a focus on early diagnosis and personalized treatment strategies. By prioritizing these areas, improvements can be achieved in patient outcomes and the management of the impacts of rheumatic and musculoskeletal diseases on individuals and society as a whole.

This Special Issue will discuss the use of diagnostic tools in rheumatic and musculoskeletal diseases, focusing on new imaging techniques and laboratory tests. The management of the above-mentioned pathologies include not only pharmacological treatments but also rehabilitation approaches including modern technologies (assistive technologies, virtual motion, robotic-assisted systems, or interactive wearable systems).

The integration of artificial intelligence (AI) and modern technologies into the diagnosis and rehabilitation of rheumatic and musculoskeletal diseases marks a significant advancement in medical practice. These innovations are transforming how healthcare providers approach these complex conditions, ultimately improving patient outcomes and enhancing the efficiency of care delivery. AI has shown great promise in the diagnostic phase by leveraging vast amounts of medical data to identify patterns that may not be immediately apparent to human clinicians. Machine learning algorithms can analyze imaging results, such as those from X-rays and MRIs, with remarkable accuracy, assisting in the early detection of conditions including rheumatoid arthritis, osteoarthritis, and other musculoskeletal disorders. By streamlining the diagnostic process, AI tools can reduce the time between initial patient presentation and definitive diagnosis, allowing for timely interventions and treatments.

In addition to diagnostics, modern technologies play a crucial role in the rehabilitation of patients with rheumatic and musculoskeletal diseases. Telemedicine platforms enable remote consultations, allowing patients to receive expert care without the need for travel, which can be particularly beneficial for those with mobility challenges. Wearable devices, such as smartwatches and fitness trackers, can monitor patients' physical activity levels and provide real-time feedback on their rehabilitation progress, promoting adherence to exercise regimens and encouraging a more active lifestyle.
For this Special Issue, we welcome original articles and reviews.

Dr. Elena Amaricai
Dr. Carmen Delia Nistor-Cseppento
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 250 words) can be sent to the Editorial Office for assessment.

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. Medicina 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 2200 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

  • osteoarthritis
  • inflammatory rheumatic diseases
  • osteoporosis
  • low back pain
  • physical medicine
  • rehabilitation
  • disability
  • quality of life

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Related Special Issue

Published Papers (5 papers)

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12 pages, 607 KB  
Article
Immersive Virtual Reality Exercise: Effects on Cortisol, Quality of Life, Cognitive Function, and Psychological Symptoms in Fibromyalgia
by Gonzalo Arias-Álvarez, María Santamera-Lastras, Dina Guzmán-Oyarzo, Waldo Osorio-Torres, Benjamín Parada-Norambuena, Daniel Pecos-Martín, Jesús G. Ponce González, José Gómez-Pulido and Claudio Carvajal-Parodi
Medicina 2026, 62(3), 446; https://doi.org/10.3390/medicina62030446 - 27 Feb 2026
Viewed by 845
Abstract
Background and Objectives: Fibromyalgia (FM) is a chronic and complex condition characterized by widespread pain, fatigue, psychological burden, and cognitive impairment, posing significant challenges for treatment. Immersive virtual reality exercise (iVRE) has been proposed as an innovative therapeutic approach to increase adherence, [...] Read more.
Background and Objectives: Fibromyalgia (FM) is a chronic and complex condition characterized by widespread pain, fatigue, psychological burden, and cognitive impairment, posing significant challenges for treatment. Immersive virtual reality exercise (iVRE) has been proposed as an innovative therapeutic approach to increase adherence, motivation, and multidimensional benefits, but evidence in FM remains limited. This study aimed to evaluate the effects of a six-week iVRE program on cortisol levels, quality of life, cognitive function, and psychological symptoms in women with FM. Materials and Methods: A quasi-experimental pre–post design was conducted with 21 women (mean age 48.1 ± 10.7 years) diagnosed with FM, who completed twelve 30 min sessions of iVRE using Oculus Quest 2™ and the FitXR platform. Outcomes assessed pre- and post-intervention included salivary cortisol (ELISA), quality of life (FIQR), emotional status (DASS-21), and cognitive function (MoCA). Adherence and safety were monitored throughout. Results: The intervention was well tolerated, with no adverse events and 100% adherence. Statistically significant improvements were observed in FIQR scores (p < 0.001, d = 3.54), depression (p < 0.001, d = 1.19), anxiety (p < 0.001, d = 1.39), and stress (p < 0.001, d = 2.28). Cognitive performance improved significantly, with higher MoCA total scores (p < 0.001, d = 1.52) and better outcomes in visuospatial ability, language, and delayed recall domains. No significant changes were detected in salivary cortisol levels (p = 1.000). Conclusions: A six-week iVRE program is safe and feasible, promoting clinically relevant improvements in quality of life, emotional well-being, and cognitive function in women with FM, despite the absence of changes in cortisol. These findings highlight iVRE as a promising complementary therapeutic strategy within multidisciplinary FM management, warranting further controlled studies with larger samples and long-term follow-up to confirm its efficacy and explore underlying mechanisms. Full article
(This article belongs to the Special Issue New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases)
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20 pages, 895 KB  
Article
Effects of Dynamic Neuromuscular Stabilization on Lower Limb Muscle Activity, Pain, and Disability in Individuals with Chronic Low Back Pain: A Randomized Controlled Trial
by Farhad Rezazadeh, Shirin Aali, Fariborz Imani, Hamed Sheikhalizadeh, Ibrahim Ouergui, Razvan-Sandu Enoiu, Luca Paolo Ardigò and Georgian Badicu
Medicina 2025, 61(11), 1961; https://doi.org/10.3390/medicina61111961 - 31 Oct 2025
Viewed by 3270
Abstract
Background and Objectives: Chronic low back pain (CLBP) is associated with altered neuromuscular control. Dynamic Neuromuscular Stabilization (DNS) targets core–limb coordination; however, its specific impact on lower-limb electromyographic (EMG) activity during gait remains unclear. Materials and Methods: Fifty-five young adults with non-specific CLBP [...] Read more.
Background and Objectives: Chronic low back pain (CLBP) is associated with altered neuromuscular control. Dynamic Neuromuscular Stabilization (DNS) targets core–limb coordination; however, its specific impact on lower-limb electromyographic (EMG) activity during gait remains unclear. Materials and Methods: Fifty-five young adults with non-specific CLBP (pain ≥ 3 months with no identifiable specific pathology) completed the trial (overall mean age 23.7 ± 1.3 years). Participants were randomized to an 8-week DNS program or a control. Pre-/Post-intervention surface EMG during gait and clinical outcomes (VAS, ODI) were assessed. Results: Compared with control, DNS showed lower adjusted Post-test VAS (3.08 ± 0.25 vs. 6.13 ± 0.24; ηp2 = 0.596) and ODI (15.73 ± 1.55% vs. 34.36 ± 1.52%; ηp2 = 0.579). Directionally, DNS was associated with phase-specific EMG modulation: tibialis anterior during mid-stance was lower (ηp2 = 0.137), rectus femoris during push-off was lower (ηp2 = 0.119), biceps femoris during push-off was lower (ηp2 = 0.168), and vastus medialis at heel-strike was higher (ηp2 = 0.077) relative to control. Other muscle–phase pairs showed no adjusted between-group differences. Conclusions: An 8-week DNS program was associated with clinically meaningful reductions in pain and disability and with phase-specific changes in lower-limb EMG during gait. These findings support DNS as a promising rehabilitation option for young adults with CLBP; confirmation in larger trials with active comparators is warranted. Full article
(This article belongs to the Special Issue New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases)
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20 pages, 4156 KB  
Article
Machine Learning Classification of Cognitive Status in Community-Dwelling Sarcopenic Women: A SHAP-Based Analysis of Physical Activity and Anthropometric Factors
by Yasin Gormez, Fatma Hilal Yagin, Yalin Aygun, Sarah A. Alzakari, Amel Ali Alhussan and Mohammadreza Aghaei
Medicina 2025, 61(10), 1834; https://doi.org/10.3390/medicina61101834 - 14 Oct 2025
Viewed by 1176
Abstract
Background and Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, has increasingly been recognized not only as a physical health concern but also as a potential risk factor for cognitive decline. This study investigates the application of machine [...] Read more.
Background and Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, has increasingly been recognized not only as a physical health concern but also as a potential risk factor for cognitive decline. This study investigates the application of machine learning algorithms to classify cognitive status based on Mini-Mental State Examination (MMSE) scores in community-dwelling sarcopenic women. Materials and Methods: A dataset of 67 participants was analyzed, with MMSE scores categorized into severe (≤17) and mild (>17) cognitive impairment. Eight classification models—MLP, CatBoost, LightGBM, XGBoost, Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and AdaBoost—were evaluated using a repeated holdout strategy over 100 iterations. Hyperparameter optimization was performed via Bayesian optimization, and model performance was assessed using metrics including weighted F1-score (w_f1), accuracy, precision, recall, PR-AUC, and ROC-AUC. Results: Among the models, CatBoost achieved the highest w_f1 (87.05 ± 2.85%) and ROC-AUC (90 ± 5.65%), while AdaBoost and GB showed superior PR-AUC scores (92.49% and 91.88%, respectively), indicating strong performance in handling class imbalance and threshold sensitivity. SHAP (SHapley Additive exPlanations) analysis revealed that moderate physical activity (moderatePA minutes), walking days, and sitting time were among the most influential features, with higher physical activity associated with reduced risk of cognitive impairment. Anthropometric factors such as age, BMI, and weight also contributed significantly. Conclusions: The results highlight the effectiveness of boosting-based models in capturing complex patterns in clinical data and provide interpretable evidence supporting the role of modifiable lifestyle factors in cognitive health. These findings suggest that machine learning, combined with explainable AI, can enhance risk assessment and inform targeted interventions for cognitive decline in older women. Full article
(This article belongs to the Special Issue New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases)
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20 pages, 2067 KB  
Article
Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis
by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem and Luca Paolo Ardigò
Medicina 2025, 61(5), 833; https://doi.org/10.3390/medicina61050833 - 30 Apr 2025
Cited by 4 | Viewed by 2526
Abstract
Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the [...] Read more.
Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. Materials and Methods: Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. Results: A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847–0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were N-acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas N-acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. Conclusions: This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice. Full article
(This article belongs to the Special Issue New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases)
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33 pages, 1511 KB  
Systematic Review
Prolotherapy as a Regenerative Treatment in the Management of Chronic Low Back Pain: A Systematic Review
by Stelian-Ilie Mociu, Andreea-Dalila Nedelcu, Andreea-Alexandra Lupu, Andreea-Bianca Uzun, Dan-Marcel Iliescu, Elena-Valentina Ionescu and Madalina-Gabriela Iliescu
Medicina 2025, 61(9), 1588; https://doi.org/10.3390/medicina61091588 - 2 Sep 2025
Cited by 3 | Viewed by 5861
Abstract
Background: Chronic low back pain markedly impairs quality of life and imposes a significant economic burden on public health. The complex pathophysiology of chronic low back pain arises from the complex anatomical configuration of the lumbar region, which includes a diverse array [...] Read more.
Background: Chronic low back pain markedly impairs quality of life and imposes a significant economic burden on public health. The complex pathophysiology of chronic low back pain arises from the complex anatomical configuration of the lumbar region, which includes a diverse array of structures. Consequently, etiologies may involve intervertebral disc degeneration, facet joint osteoarthritis, spinal stenosis, spondylosis, and spondylolisthesis. Therapeutic interventions for chronic low back pain are equally varied, ranging from pharmacological treatments to physiotherapy, kinetotherapy, balneotherapy, and image-guided local injectable procedures such as prolotherapy. Prolotherapy is a regenerative injection technique designed to stimulate the body’s healing processes by applying a regenerative treatment (typically dextrose), which aims to modulate neurogenic inflammation and diminish nociceptive signaling. Methods: A systematic review of the literature was performed in alignment with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Studies published within the last ten years evaluating the effects of prolotherapy on pain reduction in individuals with chronic low back pain were included, following a search across six databases. Results: The review revealed several studies evaluating the influence of prolotherapy on pain in chronic low back pain patients. Findings were heterogeneous, with some studies indicating significant pain reduction and others showing minimal or no improvement. Conclusions: The current evidence regarding the efficacy of prolotherapy for pain relief in chronic low back pain remains inconclusive, highlighting the necessity for further in-depth research. Continued and updated investigations into prolotherapy’s role are imperative for enhancing the quality of life of affected patients. Full article
(This article belongs to the Special Issue New Strategies for the Diagnosis and Treatment of Rheumatic and Musculoskeletal Diseases)
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