Parkinson’s disease is the second most common neurodegenerative disease worldwide, characterized by bradykinesia, rigidity, tremor, and postural instability. These symptoms often lead to significant postural deformities and an increased risk of falls, severely impacting the quality of life. Conventional rehabilitation methods have shown benefits, but recent advancements suggest that virtual reality (VR) could offer a promising alternative. This scoping review aims to analyze the current literature to evaluate the effectiveness of VR in the postural rehabilitation of patients with PD. A scientific literature search was performed using the following databases: PubMed, PEDro, Cochrane, and Google Scholar, focusing on randomized controlled trials (RCTs) published in English. Our selection criteria included studies that compared VR-based rehabilitation to traditional methods regarding posture-related outcomes. We identified and analyzed nine RCTs that met our inclusion criteria. The results consistently demonstrated that VR-based rehabilitation leads to greater improvements in balance and gait compared to conventional therapy. Key findings include significant enhancements in balance confidence and postural control and a reduction in fall rates. The superior efficacy of VR-based rehabilitation can be attributed to its engaging and immersive nature, which enhances patient motivation and adherence to therapy. VR allows for precise, repeatable training scenarios tailored to individual patient needs, providing a safe environment to practice and improve motor skills. In conclusion, VR-based rehabilitation represents an innovative approach with substantial potential to improve the quality of life for PD patients. However, limitations such as small sample sizes and short intervention durations in existing studies highlight the need for larger multicenter trials with longer follow-up periods to confirm these findings.

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively.

Background/Objectives: Tendinopathies represent a prevalent musculoskeletal condition characterized by load-dependent pain, stiffness, weakness, and impaired functionality. Current treatment includes therapeutic exercise, physical modalities and injective therapy. Hyaluronic acid (HA) is a fundamental component of the extracellular matrix and plays a crucial role in tissue hydration, elasticity, and lubrication. This study aims to evaluate the effectiveness of ultrasound-guided injections of HA in improving pain symptoms and functionality in patients with supraspinatus tendinopathy. Methods: Patients with a confirmed diagnosis of supraspinatus tendinopathy, verified through ultrasound imaging, were included in the study. Patients underwent 3 ultrasound guidance injections (1/week) of Sodium Hyaluronate (Hyalotend, 20 mg/2 mL). Patients were evaluated at the baseline, 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) after the first injection through the VAS, the QuickDASH and the SF-12. Results: Twenty-four patients were enrolled. As regards the VAS there was a statistically significant reduction (p < 0.01) of averages of values over time. The scores collected through the QuickDASH questionnaire have a statistically significant variation over time (p < 0.001). The values collected through the SF-12 Mental Component Summary (MCS) questionnaire show a highly statistically significant variation over time (p < 0.005). The values collected through the SF-12 Physical Component Summary (PCS) questionnaire show a statistically highly significant change over time (p < 0.001). Conclusions: Our results suggest that HA (Hyalotend) injections could represent a viable therapeutic option for patients with supraspinatus tendinopathy in the short, medium, and long term. Further studies with larger patient samples and a control group are needed to better investigate the effects and the modalities of administration of HA in tendinopathies.

Background: Superior cluneal nerve (SCN) entrapment is frequently underrecognized as a contributor to chronic Low Back Pain (cLBP) and gluteal pain. Musculoskeletal ultrasound may reveal surrogate markers indicative of a biomechanical entrapment environment. The primary objective was the prevalence of the ultrasound marker triad (Copeman Nod-ules-CN, thoracolumbar fascia-TLF thickening > 3 mm, and iliac enthesophytes. Secondary objectives included mean TLF thickness and its correlation with numeric pain rating scale (NPRS) and Douleur Neuropathique en 4 questions scores (DN4). Methods: In this single-center, cross-sectional observational pilot study, we enrolled 12 patients with cLBP (>12 weeks) localized to the SCN distribution and a healthy control group (12). Ultrasound measurements included TLF thickness in longitudinal and transverse planes, TLF convexity loss, iliac crest enthesophytes, and CN. Statistical analyses comprised Mann-Whitney U test, Fisher exact test, Spearman rank correlation, and multivariate logistic regression. Significance was set at p < 0.05. Results: The ultrasound marker triad (CN, iliac enthesophytes, and TLF thickening > 3 mm) demonstrated high diagnostic specificity: individually, CN were present in 91.7% of patients vs. 8.3% of controls (p < 0.001), iliac enthesophytes in 58.3% vs. 0% (p = 0.005), TLF thickening > 3 mm in 41.7% of patients vs. 0% of controls (p < 0.001)and TLF convexity loss in 100% vs. 75% (p = 0.03). Mean TLF thickness was significantly greater in patients—3.53 ± 0.46 mm longitudinal and 3.42 ± 0.39 mm transverse—compared with controls (2.61 ± 0.28 mm and 2.50 ± 0.32 mm; both p < 0.001). TLF thickness correlated strongly with NPRS (Spearman rho = 0.825; p = 0.001) but not with DN4. Logistic regression demonstrated that the marker triad accounted for 67% of NPRS variance (R² = 0.67). Conclusions: Ultrasound-detected fascial alterations and enthesopathic changes act as reliable surrogate markers for SCN entrapment and correlate strongly with nociceptive pain severity. The absence of correlation with neuropathic pain scores suggests a predominant fascial-muscular biomechanical mechanism rather than direct nerve damage. Incorporating this non-invasive protocol into clinical practice may enhance diagnostic precision and inform targeted rehabilitative strategies. Future multicenter, prospective studies with larger cohorts are warranted to validate these findings and establish standardized ultrasound criteria.

We thank Dr. Kiral for a thoughtful and constructive commentary, with which we largely agree. Surrogate biomechanical markers and direct visualisation of the SCN are complementary rather than competing: the markers were intended to extend diagnostic support to the many settings and patients in whom direct visualisation is not easy to achieve, whether for patient-related reasons or because high-resolution equipment and dedicated expertise are not universally available. We agree that these findings are supportive rather than primary diagnostic markers, and that their value may lie in the triad within a clinically pre-selected population rather than in any single sign. The nociceptive–neuropathic distinction the author emphasises is itself one of the central conclusions of our study.