Search Results (997)

Background: Walking a dog on-leash is a common activity for a large portion of our society. Many dogs consistently pull on the leash, which transmits potentially dangerous forces to the human body. The purpose of this in silico study was to determine the effects of dog-leash tension and human posture on lumbar compression, and how comparable the effects of dog walking on lumbar compression are to lifting, an activity known to contribute to low back pain. Methods: Dog-leash simulations were performed with 50–300 N directed along the arm segment of a static three-dimensional musculoskeletal model across a range of trunk segment and shoulder joint angles. Lifting simulations were performed across a range of test postures with the model holding a 50–300 N weight close to the ground. Lumbar compression was computed for each simulation using McGill’s polynomial equation and compared with the 3400 N cutoff used to develop occupational safety guidelines. Results: Lumbar compression increased as trunk segment flexion increased for all simulation conditions. With 200 N of leash tension, lumbar compression exceeded 3400 N for all postures with 25° or more of trunk segment flexion. When lifting 150 N, lumbar compression exceeded 3400 N for all postures with shank segment angle of 80° or greater and knee flexion angle of 100° or less. Conclusions: Our in silico results suggest that dog owners should seek intervention if their dog routinely pulls on the leash with a force of 200 N or greater and should attempt to lean backward when resisting leash pulling to reduce lumbar compression and injury risk. Full article

Sarcopenia is a progressive, age-related skeletal muscle disorder that serves as a driver of frailty, falls, and mortality in older adults. Despite the recent paradigm shift introduced by the latest sarcopenia consensus, which emphasizes early, proactive detection, sarcopenia cases frequently evade traditional screening due to inherent diagnostic bottlenecks and resource limitations. Artificial intelligence has emerged as a transformative solution to dismantle these barriers across the entire continuum of sarcopenia care. This review explores the rapid evolution of artificial intelligence, beginning with automated opportunistic screening that extracts prognostic musculoskeletal data from routine imaging and electronic health records, advancing toward high-precision multimodal assessment architectures. Beyond initial assessment, artificial intelligence is actively restructuring longitudinal care by the integration of ubiquitous wearables, Large Language Models, and computer vision, enabling dynamic exercise prescriptions and real-time kinematic postural correction for sarcopenia rehabilitation. Realizing this potential requires the medical community to confront urgent clinical barriers, including multi-center validation, semantic interoperability, health economic justification, and the strict preservation of human-centric ethics. By addressing these challenges, this review provides a definitive roadmap for embedding artificial intelligence across the entire sarcopenia care pathway, transforming isolated instances of opportunistic screening into a unified ecosystem for intelligent, proactive rehabilitation. Full article

Spondyloarthritis represents a group of chronic immune-mediated rheumatic diseases that manifest as peripheral or axial musculoskeletal involvement. In axial spondyloarthritis (axSpA), the milestones of structural damage are represented by inflammation, followed by erosions in the sacroiliac joints (SIJ) and new bone formation. The purpose of this narrative review is to address the unmet needs regarding targeted risk stratification of disease progression in axSpA. While studies concerning predictive biomarkers have been conducted, their use in clinical practice has not yet been validated. Analysis of disease progression in patients recently diagnosed with non-radiographic axSpA, with fulfillment of the Assessment of Spondyloarthritis International Society criteria, determined a mean time of structural changes progression of 2.4 years. While factors such as human leukocyte antigen (HLA)-B27 and C-reactive protein are useful in classifying patients into risk categories regarding radiographic progression, novel biomarkers are needed in clinical practice to further facilitate treatment strategy selection. Choosing biomarkers to analyze the potential of both spinal and SIJ radiographic progression is useful in monitoring patients and reducing the burden of disease. Fetuin-A, sclerostin, and autoantibodies against Cluster of Differentiation 74 (anti-CD74) were associated with SIJ changes in various studies. Regarding spinal structural damage, adipokines, particularly leptin and visfatin, have been extensively studied and have shown promising results. Dickkopf-1, a regulator of the Wnt signaling pathway, vascular endothelial growth factor, and matrix metalloproteinase-3 have also presented associations with worsening modified Stoke Ankylosing Spondylitis Spine Score. The potential of each biomarker may be heightened by their use in prediction models with the purpose of implementation in clinical practice, particularly in improving patient outcomes and tailoring treatment strategies for individuals with spinal structural damage. Full article

Background: Advances in hemophilia treatment, including extended half-life factor concentrates, non-replacement therapies, and gene therapy, have substantially reduced bleeding frequency and improved life expectancy. However, persistent musculoskeletal damage, subclinical bleeding, and residual arthropathy remain important clinical challenges despite improved hematologic control. Objective: We aimed to analyze recent therapeutic advances in hemophilia, examine persistent musculoskeletal complications, and propose a multidisciplinary care model based on structural monitoring, highlighting the role of musculoskeletal ultrasound. Methods: A structured narrative review with a reproducible search strategy was conducted following PRISMA 2020-informed methodological principles. PubMed/MEDLINE and Scopus were searched for clinically relevant studies published between 2018 and 2026 focusing on advanced hemophilia therapies, musculoskeletal complications, and structural monitoring. A total of 478 records were identified, and 13 studies were included after screening and selection using the Rayyan platform. Results: Modern therapies markedly reduce clinically evident hemarthroses, but structural joint alterations and subclinical disease activity may persist, particularly in patients with pre-existing arthropathy. Imaging-based studies identified persistent synovial and osteochondral alterations despite effective hematologic control. Magnetic resonance imaging remains the reference standard for structural assessment, although its routine use may be limited by accessibility and cost. Musculoskeletal ultrasound emerges as an accessible and reproducible tool for dynamic joint evaluation and early detection of structural alterations, supporting longitudinal monitoring and individualized rehabilitation strategies. Conclusions: In the era of advanced therapies, comprehensive hemophilia management requires not only effective hematologic control but also structured musculoskeletal follow-up. The integration of musculoskeletal ultrasound into multidisciplinary care models may favor earlier detection of joint alterations and more individualized rehabilitation strategies. Full article

(1) Background: The global expansion of teleworking has increased concern regarding musculoskeletal pain associated with home-based working conditions. This study quantified the prevalence of musculoskeletal pain among Brazilian teleworkers and explored ergonomic and environmental factors associated with the distribution of physical symptoms and the multisite pain burden. (2) Methods: A cross-sectional survey was conducted between June and August 2024, analyzing 184 valid responses from teleworkers across various professional sectors. Data were collected via an online questionnaire assessing sociodemographic characteristics, workstation ergonomics, and musculoskeletal symptoms using the Nordic Musculoskeletal Questionnaire (NMQ). Statistical analyses included Pearson’s chi-square tests, logistic regression, and exploratory Random Forest modeling to prioritize predictors. (3) Results: Musculoskeletal pain was reported by 74% of participants, with the lower back (40.8%), neck (36.4%), and upper back (30.4%) being the most frequently affected anatomical regions. The primary ergonomic and environmental factors associated with pain reports included discomfort with the desk and mouse, suboptimal thermal comfort, and prolonged sitting. Odds ratios demonstrated strong statistical co-occurrence between recent and 12-month pain reports, particularly for the shoulders, reflecting overlapping recall indicators rather than temporal symptom progression. (4) Conclusions: Musculoskeletal pain is highly prevalent among Brazilian teleworkers, showing clear links to localized workstation inadequacies and overlapping short- and long-term symptom reporting. These findings highlight the need for targeted institutional occupational health policies, such as ergonomics training and adjustable furniture provision, while future longitudinal research remains essential to confirm causal pathways. Full article

Newly hired workers in construction, industrialized building production, and other built-environment operations often face elevated safety and ergonomic risk while learning manual tasks. At the same time, many onboarding programs still rely on observation, verbal coaching, and checklist-based sign-off, which can be difficult to standardize across supervisors and sites. This study presents the development and field evaluation of a data-driven training system that integrates markerless motion capture, machine-learning-assisted ergonomic risk scoring, and Lean/continuous-improvement (CI) routines to provide structured coaching during onboarding. A single-site, non-randomized quasi-experimental sequential-cohort design compared a traditional onboarding cohort with a subsequent app-supported cohort (n = 20 each). Primary outcomes were time to qualification, training cost, and task accuracy. Secondary site-level indicators were safety compliance and musculoskeletal (MSK) injury outcomes. Compared with the traditional cohort, the app-supported cohort reached qualification sooner (5.85 ± 1.50 vs. 18.60 ± 3.50 calendar months), at lower cost (SR 29,250 ± 7602 vs. SR 93,000 ± 17,348 per employee), and with higher task accuracy (88.60 ± 5.70% vs. 60.65 ± 10.60%). Welch’s t-tests showed statistically significant differences across all primary outcomes (all p < 0.001), although the standardized effect sizes were very large and should be interpreted cautiously given the modest sample and non-randomized design. Safety compliance (+68%) and MSK injuries (−25%) are reported only as descriptive site-level indicators because denominator and exposure data were not available for inferential analysis. The study contributes a practical intervention model linking ergonomic sensing to coaching cues, auditable training logs, A3 problem solving, and standard work refinement. The findings suggest that this integrated approach is promising for built-environment onboarding, but multi-site studies with stronger comparative designs, individual-level reporting, and fuller algorithmic documentation are needed. Full article

Background: Hip implant fractures are rare, yet difficult to correct once they occur. For cemented implants, fracture is often associated with increased stresses at the implant stem when proximal regions of the implant have debonded. While deterministic analyses offer predictive power by using averages for model inputs, averages fail to capture the variability inherent in device manufacturing and musculoskeletal biology. This study developed a probabilistic finite element model of a debonded hip implant to better account for some of these variabilities to predict the most likely failure mode. The hypothesis was that fatigue would be more likely to occur than overloading. Methods and Materials: Monte Carlo sampling generated 1000 simulations varying the material elastic modulus (implant, cement, and bone) and loading magnitude at stance phase of the gait. The resultant distributions of maximum von Mises stress at the stem were compared to distributions for failure properties in the literature. Results: The analysis found the likelihood of the implant failing due to overloading was remote. In contrast, fatigue failure had a 99.4% chance of occurring. Fracture mechanics predicted that the debonded implant would reach critical flaw length between 1.8 and 26.4 months, with a mean of 7.2 months. Conclusions: The results show good agreement with the findings of the case study the model was based on, particularly in predicting the location of failure and fatigue life. The results of this study provide a framework for developing future decision-making tools that ultimately may assist clinicians in deciding when interventions are necessary to minimize the risk of implant or periprosthetic fracture. Full article

Background/Objectives: Digital twin technology has attracted growing attention in orthopedics for its potential to support patient-specific modeling, simulation, and data-driven clinical decision-making. However, despite the rapid growth in the literature, clinical adoption remains limited, and the term “digital twin” is often applied inconsistently to fundamentally different technological approaches. To establish a clear, function-oriented definition and taxonomy of digital twins in orthopedics, to map current applications across subspecialties, and to critically assess the level of clinical evidence supporting their use. Methods: A structured narrative review was conducted using targeted searches of major bibliographic databases (PubMed, Web of Science, Scopus), publisher platforms, and complementary semantic search tools. The retrieved literature was interpreted using a functional analytical framework focusing on patient specificity, data integration, intended clinical role, and degree of clinical validation. Rather than conducting a formal, systematic appraisal, the aim was to provide a concept-driven synthesis of the field and identify patterns of use, maturity, and translational limitations. Results: Most reported orthopedic digital twin implementations appear to represent static patient-specific simulations supported primarily by preclinical or feasibility-level evidence. Monitoring-oriented digital twins have been more commonly reported in spine care, rehabilitation, and sports medicine, enabling longitudinal assessments but offering limited predictive or decision-support value. Decision-oriented digital twins are uncommon, yet they seem to be the most clinically mature type described in the current literature; so far, only one randomized controlled trial has demonstrated improved decision quality in arthroplasty care. Fully integrated hybrid or closed-loop digital twins remain largely experimental. Conclusions: Digital twin technology in orthopedics is characterized by substantial conceptual heterogeneity and limited clinical validation. Near-term clinical impact is most likely to arise from narrowly focused, decision-oriented, and monitoring-based digital twins, although this projection remains dependent on further clinical validation. Greater definitional clarity, functional transparency, and rigorous clinical evaluation are essential to support meaningful translation into routine orthopedic practice. Full article

Preliminary estimation of lower-limb motor function is important in rehabilitation research, especially for biomechanical assessment of post-stroke hemiplegic gait. However, subject-specific musculoskeletal modeling in this population is challenging because standard maximum voluntary contraction (MVC) testing is often unsafe or unreliable for normalizing surface electromyography (sEMG) signals. To address this limitation, a normalized correction coefficient was introduced for pathological sEMG preprocessing, and an improved Hill-type muscle model (iHMM) was established to account for submaximal activation conditions. By combining inverse dynamics, static optimization, and a subject-specific lower-limb dynamic model, the proposed framework was used to estimate musculotendon force, knee joint torque, knee joint kinematics, and shank center-of-mass trajectory. In a preliminary validation involving six hemiplegic subjects, the predicted knee joint torques showed moderate to good agreement with the reference results, with correlation coefficients ranging from 0.724 to 0.807 and RMSE values ranging from 3.872 to 7.814 Nm. These preliminary results support the feasibility of the proposed framework for subject-specific biomechanical analysis of the hemiplegic lower extremity and suggest its potential utility in individualized rehabilitation assessment. Full article

Artificial intelligence (AI) is increasingly reshaping musculoskeletal (MSK) imaging across the entire imaging pathway. This narrative review summarizes current AI applications in MSK radiology across four domains: acquisition and reconstruction, detection and triage, characterization and quantification, and prognosis and decision support. AI-based reconstruction has enabled faster MRI acquisitions, improved denoising and artifact reduction, and supported low-dose CT imaging while preserving diagnostic quality. Fracture detection and triage currently represent the most mature clinical applications, particularly in emergency settings. AI is also promoting a shift from qualitative interpretation to quantitative imaging phenotyping through automated assessment of body composition, cartilage, bone density, degenerative spine disease, skeletal maturity, and lesion heterogeneity. Emerging applications in prognostic modeling, implant evaluation, and multimodal risk stratification remain promising but less mature. Broader clinical implementation is still limited by restricted interpretability, dataset bias, insufficient prospective validation, regulatory complexity, and unresolved medico-legal issues. Overall, AI should be viewed as a tool to augment, not replace, radiological expertise. Full article

Playing-related musculoskeletal disorders (PRMDs) are highly prevalent among bass-guitar players due to sustained asymmetrical postures, repetitive finger movements, and prolonged support of instrument weight. This study proposes a Human Digital Biomechanical Twin-driven, simulation-based approach to optimize bass-guitar support systems, integrating biomechanical modelling, motion capture, and musculoskeletal simulation. A preliminary survey among 63 Italian bass-guitar players was performed to define the experimental conditions regarding posture, instrument type, and session duration. Fifteen experienced bassists participated in laboratory trials using motion capture and postural assessment tools, including MediaPipe Pose, RULA, and AnyBody Modelling System. Baseline results highlighted significant activation of the trapezius and spinal extensor muscles (19–26% MVC), confirming high ergonomic risk. Three alternative support configurations were digitally simulated, revealing that a three-point harness system (bilateral shoulder straps plus thoracic anchoring) reduced spinal stabilizer activation by 15–25% across four anthropometric percentiles. Experimental validation confirmed enhanced comfort, reduced fatigue, and improved instrument stability, with the majority of participants preferring the ergonomic configuration. These findings demonstrate the feasibility of a simulation-based, prospective, and human-centred ergonomic design framework, offering a scalable methodology to compare and optimize adaptive instrument-support systems before physical prototyping. Full article

Background/Objectives: Healthy longevity depends on not only lifespan but also the maintenance of physiological, metabolic, physical, and cognitive functions throughout aging. Identifying early determinants of health is crucial for preventing age-related decline. This narrative review aims to synthesize current evidence on how diet and specific nutrients relate to these early risk factors and indicators of healthy longevity. Methods: A review was performed to identify the links between dietary factors, energy balance, and gut microbiota composition and normal body weight; blood cholesterol, pressure, and glucose; healthy sleep; an active lifestyle; and normal physical function and cognitive performance. Particular attention was given to Mediterranean and other plant-based dietary models as sources of key nutrients. Evidence from observational studies, randomized controlled trials, and meta-analyses was considered. Results: Across all markers, dietary quality and nutrient adequacy emerged as consistent determinants of health outcomes. Key nutrients were associated with favorable cardiometabolic, cognitive, and musculoskeletal functions, such as omega-3 fatty acids, fiber, vitamins D and B, minerals like magnesium and potassium, and polyphenols. Common nutrition gaps included insufficient intake of fiber, unsaturated fats, and micronutrients, which was often linked to a shift toward less plant-based diets. Gut microbiota diversity may mediate several of these associations, influencing metabolism, inflammation, sleep quality, and cognitive performance, although inter-individual variability and causal pathways remain incompletely understood. Conclusions: An integrated dietary approach emphasizing the consumption of whole and plant-rich foods, with moderate amounts of animal foods, supports multiple early markers, risk factors, and indicators of healthy longevity. The modulation of the gut microbiota through plant-based diets and fermented foods represents a promising strategy for maintaining health across aging trajectories. Full article

Alkaptonuria (AKU) is a rare inherited metabolic disorder characterized by progressive musculoskeletal damage, chronic pain, and functional heterogeneity. To better quantify this variability, we introduced the concept of the functional age gap, defined as the difference between chronological age and a data-derived estimate of functional age. The study included 134 patients with AKU from the ApreciseKUre database. Functional age was calculated by mapping Health Assessment Questionnaire Disability Index (HAQ-DI) and Knee Injury and Osteoarthritis Outcome Score (KOOS) values to age-referenced normative data. Most patients (94.8%) showed a positive functional age gap, with a mean difference of 15 years, which indicates a functionally older profile than expected for their chronological age. A bagging ensemble of decision trees was then used to explore relationships between clinical variables and functional age gap severity. The model achieved moderate but stable classification performance across repeated stratified cross-validation (64%), consistent with an exploratory analysis in a small rare-disease cohort. SHapley Additive exPlanations analysis identified age, AKUSSI spinal pain, AKUSSI joint pain, Schober test, and hip and knee activity as the most influential predictors. These findings support the functional age gap as an interpretable, hypothesis-generating descriptive metric for functional assessment in AKU, while its predictive utility for individual patient stratification will require validation in larger and longitudinal cohorts. Full article

Purpose: Large language models (LLMs) may reduce administrative workload in radiology by automating structured diagnostic extraction from text reports. This study evaluates the accuracy of ChatGPT-4.0 when extracting correct diagnoses from musculoskeletal (MSK) radiology text reports, and compares its performance with that of experienced human readers, using cluster-adjusted and consensus-level analyses. Materials and Methods: Twenty-three multimodal MSK imaging cases (X-ray, ultrasound, CT, and MRI) were analysed. Ten human readers and ChatGPT-4.0 (10 independent iterations) provided primary (1st) and secondary (2nd) diagnoses from six predefined options. We analysed data at the individual-reader level using cluster-adjusted generalised estimating equations (GEE) and at the case level using majority consensus with exact McNemar testing. Within-case (α_case) and within-reader (α_reader) correlations and design effects were calculated to assess clustering and implications for sample size. Results: For 1st diagnoses, AI accuracy was 0.957 (95%–CI 0.922–0.976) versus 0.865 (95%–CI 0.815–0.903) for human readers (absolute difference −0.091; OR 3.43, 95%–CI 1.07–11.02; p = 0.038). Within-case correlation (α case = 0.247) exceeded within-reader correlation (α reader ≈ 0); this resulted in a design effect of 5.7 and an effective sample size of 80.7. At the consensus level, discordance occurred in 2/23 cases (8.7%), with no significant difference between methods (McNemar p = 1.00). When 1st and 2nd diagnoses were combined, both systems achieved 23/23 correct consensus classifications. Interrater reliability between AI and human classifications was almost perfect (Gwet’s AC1 = 0.836–0.927). Conclusions and Key points: In this structured MSK text-report setting, ChatGPT-4.0 achieved diagnostic accuracy comparable to that of experienced radiologists, with modest individual-reader advantages that disappeared under consensus aggregation. Clustering analysis indicates that variability is primarily case-driven, suggesting that future validation studies will benefit more from expanding case numbers than reader numbers. Our data suggest that large performance divergences between AI and human consensus are unlikely in similar structured diagnostic contexts. Full article

Accurate assessment of the A1 pulley is essential for the diagnosis and treatment of trigger finger, particularly in ultrasound-guided percutaneous release. Although high-resolution ultrasound is widely used to evaluate pulley morphology, the validity of sonographic measurements of A1 pulley length has not been clearly established against anatomical reference standard. This study evaluated the reliability and validity of ultrasound for measuring A1 pulley length and thickness in human cadavers and assessed the reproducibility of A2 pulley length. Twenty fingers from five fresh-frozen cadaveric hands were examined. Two blinded expert musculoskeletal sonographers independently performed ultrasound acquisition and measurements of A1 and A2 pulley length and A1 pulley thickness, while a third blinded observer obtained anatomical measurements after meticulous dissection using a digital caliper. Ultrasound systematically overestimated A1 pulley length compared with anatomical measurements and showed very poor reliability (ICC = 0.05) with wide limits of agreement. In contrast, A2 pulley length showed high interobserver reliability (ICC = 0.83) and relatively better agreement with anatomical values, whereas A1 pulley thickness showed moderate reproducibility (ICC = 0.61). Overall, A1 length measurements showed substantial variability and limited agreement, while A2 length and A1 thickness appeared more consistent within this experimental setting. These findings should be interpreted within the limitations of a cadaveric model. Full article