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: 15 December 2025 | Viewed by 903

Special Issue Editors


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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

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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 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

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Keywords

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

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Published Papers (1 paper)

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Research

20 pages, 2067 KiB  
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
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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
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