Search Results (190)

This study presents a combined numerical–statistical framework based on the Extended Finite Element Method (XFEM) and the Taguchi optimization method to assess the fracture behavior of pressurized pipelines containing external longitudinal cracks. XFEM is employed to evaluate the local fracture response without remeshing, while the Taguchi method is used to quantify the influence of key parameters and identify an optimal configuration with a limited number of simulations. The control parameters considered are internal pressure, initial crack length, and wall thickness, and the evaluated mechanical responses include circumferential stress, the J-integral, and the stress intensity factor. The optimization follows the “smaller-the-better” criterion to minimize stress concentration, fracture-driving forces, and the risk of structural failure. Results indicate that internal pressure predominantly affects circumferential stress and the stress intensity factor, whereas wall thickness has the greatest influence on the J-integral. The optimal parameter combination is determined through signal-to-noise ratio analysis and validated using the delta method, confirming the robustness of the selected configuration. A confirmation simulation performed with XFEM demonstrates a consistent reduction in all fracture-related mechanical responses, highlighting the effectiveness of the proposed approach. It should be noted that the present study is limited to the static fracture assessment of external cracks and does not address fatigue crack growth or fatigue life prediction. Overall, the proposed methodology provides a decision-support tool for pipeline integrity management by integrating numerical fracture mechanics analysis with robust design optimization, thereby contributing to safer operation and improved structural reliability. Full article

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

This paper presents a new process for forming stepped shafts by upsetting with two movable deformation zones. The developed technology enables several shaft steps to be formed at the same time, thereby increasing process efficiency and reducing material consumption. A distinctive feature of the process is that it uses two forming sleeves, each with a variable cross-section of the impression, which move in an opposite direction to that of the punches during operation. This results in a simultaneous occurrence of upsetting and extrusion, thus leading to intensified plastic deformation and stabilized metal flow. The practical applicability of the process is demonstrated on the example of a forged railway axle. An analysis is carried out by the finite element method (FEM) using specimens of hot-formed C35 steel. The obtained results reveal proper material flow and the correct filling of the tool impressions. The examination of strain and stress distributions confirms favorable forming conditions. The calculated values of the Cockcroft–Latham integral indicate favorable forming conditions and a low risk of fracture initiation during the analyzed process. The results demonstrate the potential of the proposed technology and provide a basis for future experimental verification and industrial assessment. Full article

Lower back pain (LBP) is a leading cause of disability globally, characterized by multifactorial origins that complicate accurate diagnosis and effective treatment planning. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and radiomics, has shown promise for improving the reproducibility and quantitative assessment of spine neuroimaging. This narrative review synthesizes current evidence on AI-derived imaging biomarkers in magnetic resonance imaging (MRI) and computed tomography (CT), with emphasis on disc degeneration, spinal stenosis, endplate signal abnormalities, paraspinal muscle composition, vertebral fractures, and spinal alignment. AI-based reconstruction, segmentation, and classification methods may reduce reader variability and enable standardized quantification of imaging features. However, the current evidence base remains dominated by technical and retrospective validation studies, and high diagnostic performance should not be interpreted as proof of improved patient-centered outcomes. The present review distinguishes technical feasibility, diagnostic assistance, prognostic association, and clinical utility, and highlights the persistent efficacy-effectiveness gap in AI-based spine imaging. Although multimodal models integrating imaging, clinical, biomechanical, and patient-reported data may improve future risk stratification, clinical translation remains constrained by heterogeneous datasets, limited external validation, incomplete interpretability, and evolving regulatory frameworks. Prospective multicenter validation and outcome-linked evaluation are required before AI-derived imaging biomarkers can be considered established tools for routine LBP management. Full article

Background/Objective: Long-term tenofovir disoproxil fumarate (TDF) therapy is associated with progressive bone mineral density loss in patients with chronic hepatitis B (CHB), yet existing fracture risk algorithms, such as FRAX, were not designed for this population. We aimed to develop and internally validate a clinical nomogram for identifying TDF-associated osteoporosis using penalized regression on demographic, virological, and biochemical predictors. Methods: In this single-center retrospective cohort study, 237 adult CHB patients receiving TDF for at least 12 months underwent dual-energy X-ray absorptiometry (DXA). Osteoporosis was defined as a T-score of −2.5 or lower at the lumbar spine or femoral neck. Thirteen candidate predictors were evaluated using LASSO regression with 10-fold cross-validation; selected variables were entered into an unpenalized multivariable logistic regression model; internal validation employed bootstrap resampling with 200 replications to derive optimism-corrected estimates of discrimination and calibration. The clinical utility was assessed using decision curve analysis (DCA). Results: Osteoporosis prevalence was 15.2% (n = 36). LASSO selected three predictors: prior fragility fracture (OR 11.45, 95% CI 4.82–27.15), the Charlson Comorbidity Index (OR 1.45 per unit, 95% CI 1.15–1.85), and alkaline phosphatase. The model demonstrated strong discrimination (apparent C-index 0.860; optimism-corrected 0.845) with excellent calibration (slope 0.94, intercept 0.02; Brier score 0.095). At a 0.15 probability threshold, sensitivity was 86.0%, specificity 78.0%, and negative predictive value 97.0%. DCA confirmed superior net clinical benefit over default strategies across the 0.10–0.30 threshold range; a pre-specified sensitivity analysis excluding fracture history retained meaningful discrimination (corrected C-index 0.791). Conclusions: This nomogram offers a clinically actionable, disease-specific tool for stratifying osteoporosis risk in TDF-treated CHB patients, particularly well suited for safely deferring DXA imaging in low-risk individuals. External validation in multicenter and ethnically diverse cohorts is required before widespread implementation. Full article

Background: Osteoporosis/osteoporotic fractures are identified in both hypercalcemic (HC-HPT) and normocalcemic variant (NC-HPT) of primary hyperparathyroidism (HPT) at various rates. Objective: Noting the need of modern society to easily assess the osteoporotic fracture risk amid the diagnosis of HPT, we aimed to address this gap by analyzing the 10-year fracture risk assessment based on traditional FRAX (Fracture Risk Assessment Tool) model in comparison to the novel algorithm (FRAXplus), according to the adjustment for the presence of HPT, as well as for the use of lumbar bone mineral density (BMD) in menopausal women with HPT versus controls (non-HPT), respectively, between HC-HPT versus NC-HPT. Methods: For each patient, the latest algorithms of FRAX and FRAXplus provided the 10-year fracture risk for major osteoporotic fractures (MOF) and for hip fracture (HF) amid a single-center, retrospective, real-life study. Results: In total, 131 subjects were included: 51.15% had HPT (64.18% of them had HC-HPT) versus age-, menopause duration-, and body mass index-matched (HPT-free) controls. As a result, 10-year fracture risk for MOF and HF was statistically significantly higher in HPT versus controls only for the calculation with femoral neck BMD. FRAXplus showed that for both estimations (MOF and HF) with introduction of lumbar BMD remained higher than controls (4.55% vs. 3.7%, p = 0.004, respectively, 1.05% vs. 0.5%, p = 0.002). In HPT group, 10-year fracture risk for MOF and HF were higher if adjustment for HPT was applied. The highest 10-year fracture risk for MOF was obtained for HPT adjustment with femoral neck BMD (5.9%) versus the estimation without using femoral neck BMD (5.25%, p = 0.001), respectively, versus the probability with adjustment for lumbar BMD (4.55%, p < 0.001). The same observation was for HF: 1.4% versus 1.2% (p = 0.028), respectively, versus 1.05% (p < 0.001). In HPT group, parathormone level positively correlated with 10-year hip fracture risk with HPT adjustment, without femoral neck BMD (r = 0.257, p = 0.049). Bone formation marker P1NP negatively correlated with 10-year fracture risk for MOF without femoral neck BMD (r = −0.416, p = 0.043), respectively, with the estimation including HPT adjustment without femoral neck BMD (r = −0.404, p = 0.05), and with the 10-year HF risk calculated without femoral neck BMD (r = −0.407, p = 0.049). Conclusions: To our best knowledge, this is the first study to address the use of FRAXplus in HPT. The similar values between FRAX-based probabilities without the use of femoral neck BMD in HPT versus non-HPT controls suggested that this traditional estimation might not be so useful in HPT population, thus the need for novel models (HPT adjustment). HPT adjustment (FRAXplus) provided a higher MOF/HF risk versus non-adjustment (FRAX). All 10-year probabilities based on FRAX and FRAXplus models showed similar values in HC-HPT versus NC-HPT, which implies that current algorithms might not make a clear distinction between HPT subtypes, yet the statistically significant results within each of these subgroups sustain the FRAXplus application regardless of the variant. Full article

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources. Full article

Fibrous dysplasia is one of the most common skeletal lesions. The wide spectrum of clinical manifestations ranges from asymptomatic conditions (typical of monostotic forms) to severe skeletal diseases with deformity and fractures for polyostotic fibrous dysplasia. The classical radiological features include: an osteolytic geographic pattern, ground-glass bone matrix, cortical thinning/cortical scalloping, bone deformities and enlargement, concavity of margins (evaluated with MRI), and cystic areas (MRI). All the bones can be affected, and the proximal femur is the most common one (about 30% of cases). Nonetheless, the disease can also affect cranio-facial bones, leading to compression of neural structures, as well as deformation and enlargement of facial bones, leading to the so-called “leontiasis ossea” or “facies leonine”. The polyostotic forms of fibrous dysplasia can be associated with multiple soft-tissue myomas (Mazabraud syndrome) or several endocrine diseases (McCune–Albright syndrome). In every diagnostic step of the disease, as well as in different fibrous dysplasia forms, imaging plays a key role. Indeed, radiology is fundamental to assess the suspicion of fibrous dysplasia in classical monostotic forms, representing the sole diagnostic tool needed in many cases. Imaging is also fundamental to staging and following up on more severe polyostotic forms, as well as for detecting complications. In this comprehensive updated review article, we examine every aspect of the disease, with a main focus on imaging presentation. The indications for biopsy are discussed as well. Most importantly, the article details the potential risk of malignant transformation (osteosarcoma, fibrosarcoma, chondrosarcoma, and other rarer sarcomas, all accounting for <1% of cases) underlying the radiological patterns of these conditions. The occurrence of aneurysmal bone cyst-like changes on fibrous dysplasia is also analyzed in the article. This review article aims to be a comprehensive guide for radiologists and clinicians involved in the care of patients affected by various forms of fibrous dysplasia, and a starting point for future research. Many classical and atypical cases are collected as an iconographic comprehensive representation. Full article

Background: Malnutrition and cognitive impairment are common in patients with proximal femur fractures and are associated with adverse outcomes. However, the prognostic performance of nutritional screening tools in this population remains uncertain. Methods: In this retrospective cohort study, 1595 patients aged ≥ 60 years undergoing surgery for proximal femur fracture were included. Nutritional status was assessed using seven established scores (MNA, PNI, GNRI, MUST, GMS, NRS-2002, CONUT). Mortality was evaluated at 30 days, 3 months, 6 months, 1 year, and 2 years. Associations were analysed using multivariable logistic regression adjusted for age, sex, ASA class, institutional residence, fracture type, and cognitive impairment. Discrimination was assessed using the area under the curve (AUC), and calibration was evaluated using calibration slopes and Brier scores. Results: Worsening nutritional status was consistently associated with increased mortality across all scores and timepoints. The strongest gradient was observed for MNA, with 2-year mortality increasing from 15.6% in patients with normal nutritional status to 53.8% in malnourished patients. Most scores remained independently associated with mortality after adjustment, with odds ratios per 1 SD deterioration ranging from 1.3 to 1.6. Discriminative performance was modest (AUC 0.57–0.69), with MNA showing the highest values. Differences between scores were small, with overlapping confidence intervals. Calibration was good across all models at 1 and 2 years. Conclusions: Nutritional status is independently associated with mortality after proximal femur fracture but provides limited discrimination for individual risk prediction. Nutritional scores may support identification of vulnerable patients but demonstrated limited performance for individual mortality prediction. 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

Background/Objectives: Inflammatory bowel disease (IBD) is associated with reduced areal bone mineral density (aBMD) and an increased risk of osteoporosis and fragility fractures. Although exercise improves bone health in the general population, its effects on aBMD in adults with IBD are unclear. This systematic review aimed to evaluate the effects of structured exercise interventions on aBMD in adults with IBD and to assess the certainty of the evidence. Methods: We conducted a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook. Searches were performed in CENTRAL, MEDLINE, Scopus, and Web of Science from inception to November 2025. We included randomized controlled trials comparing structured exercise interventions with usual care, no structured exercise or no intervention in participants aged 16 years and older with IBD. The primary outcome was aBMD; physical activity was a secondary outcome. Risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 2.0), and certainty of evidence was evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). The review protocol was registered in International Prospective Register of Systematic Reviews (PROSPERO) CRD42024617200. Results: Two randomized controlled trials (n = 164), both conducted exclusively in adults with Crohn’s disease, met the inclusion criteria. Combined impact and resistance training for 6 months was associated with greater lumbar spine aBMD compared with usual care, while hip outcomes were not consistently improved. A 12-month low-impact exercise program compared with no intervention suggested greater trochanter aBMD gain among fully compliant participants, but intention-to-treat between-group differences were not statistically significant across skeletal sites. Due to heterogeneity in interventions and reporting, meta-analysis was not performed. Overall certainty of the evidence was very low because of methodological limitations and imprecision. Conclusions: We are very uncertain about the effect of exercise interventions on aBMD in adults with IBD. Current randomized evidence is limited to adults with Crohn’s disease and is insufficient to determine the optimal exercise modality, frequency, intensity, progression, or loading characteristics for improving bone health. Well-designed trials across IBD phenotypes are needed to clarify the role of exercise in bone health management in IBD. Full article

Background: Missed fractures on initial imaging assessments remain a clinically significant source of diagnostic errors in orthopedic and trauma care. AI-assisted imaging tools are increasingly integrated into fracture detection workflows. However, their diagnostic benefits and safety vary substantially across anatomical regions, clinical contexts, and levels of reader experience. Purpose: To synthesize the current evidence on the diagnostic impact of AI-assisted fracture detection and to discuss evidence-informed principles for safe and selective clinical deployment. Methods: A structured narrative synthesis of meta-analyses, multi-reader, multi-case observer studies, and real-world implementation investigations was performed. Diagnostic performance patterns were examined across anatomical regions and levels of reader experience. No quantitative pooling or reanalysis of the primary data was performed. The findings were synthesized across anatomical regions, reader-experience groups, and implementation-relevant clinical contexts. Results: Across studies, AI-assisted interpretation was generally associated with moderate gains in sensitivity and lower missed-fracture rates compared with unaided human reading, while largely preserving specificity. The diagnostic benefit was greatest among less-experienced readers in high-volume emergency settings. Performance was strongly anatomy-dependent: consistent and clinically meaningful improvements were observed for hip and appendicular skeleton fractures; intermediate benefits with increased false-positive burden were reported for wrist and rib fractures; and inferior sensitivity relative to expert interpretation was documented for cervical and vertebral spine injuries. Conclusions: AI-assisted fracture detection improves diagnostic safety when implemented as a structured second-reader tool; however, its effectiveness depends heavily on anatomy. Available evidence supports selective, risk-stratified deployment, guided by anatomy-specific risk considerations and supervised clinical use, rather than indiscriminate or autonomous use, to maximize benefits and minimize patient safety risks in orthopedic and trauma imaging. Full article

Background: Postoperative anemia associated with intertrochanteric hip fracture is a detrimental complication that detrimentally impairs patients’ outcomes. This study is designed to develop an online predictive tool to assist physicians in developing surgical blood preparation strategies to prevent the occurrence of postoperative anemia. Methods: This study included data collected from June 2017 to June 2025 on intertrochanteric hip fracture patients at Tangdu Hospital, including demographic information, comorbidities, vital signs, and laboratory results. LASSO regression was used to select predictive variables, and seven machine learning techniques: Logistic Regression, Support Vector Machine, Decision Tree, LightGBM, XGBoost, Neural Networks, and Random Forest, were compared to identify the best tool for predicting postoperative anemia risk. We created a patient-specific risk prediction tool with SHAP-driven interpretability for clinical decision support. Results: A total of 815 patients were included in the analysis, of whom 208 (25.5%) presented with postoperative anemia. Eight variables were selected to build seven machine learning models. Among these, the SVM model exhibited the best predictive performance in terms of discrimination, calibration, and clinical applicability, with an AUC range of 0.827–0.831. In test sets encompassing diverse population characteristics, SVM achieved the highest sensitivity (72.73%), accuracy (77.78%), and F1 score (57.14%). Conclusions: We established an online prediction platform for clinical practice, enabling clinicians to assess anemia risk in intertrochanteric hip fracture patients and support early prevention of postoperative anemia. Full article

Background: Osteoporosis is a prevalent condition characterized by low bone mass and altered microarchitecture, increasing fracture risk. Early detection remains challenging, as conventional methods such as DXA are limited to specialized settings and often detect disease only after a fracture. Radiomics and three-dimensional (3D) imaging techniques, such as CBCT, may provide novel approaches for assessing bone quality. Methods: This pilot study analyzed 68 CBCT scans from adult patients (41 females, 27 males; mean age 57 years). Three-dimensional regions of interest (ROIs) were delineated in seven maxillofacial and mandibular sites (total 309 ROIs). Radiomic texture features were extracted and compared with corresponding T-scores from DXA measurements. Additionally, synthetic 3D reference phantoms with controlled variations in density, trabecular connectivity, and structural anisotropy were generated to evaluate the sensitivity of texture features to microarchitectural changes. Results: Several radiomic features, including GLCM-, ARM-, and Gradient-derived parameters, demonstrated consistent monotonic trends correlating with bone density and microstructural deterioration. Differences in feature values were observed across healthy, osteopenic, osteoporotic, and advanced osteoporotic states. Reference phantoms confirmed that the observed trends were attributable to structural differences rather than imaging variability. Features such as Sum Variance and Correlation exhibited potential as early indicators of microarchitectural degradation. Conclusions: Three-dimensional CBCT texture analysis may provide a non-invasive, supplementary tool for assessing bone quality and detecting early osteopenic changes. Further studies with larger cohorts are warranted to validate radiomic markers and develop predictive indices for osteoporosis screening. Full article

Patients with heavily calcified coronary arteries represent a challenge in percutaneous coronary intervention (PCI), as severe calcification impairs device delivery and limits optimal stent expansion, leading to higher risks of stent thrombosis, restenosis, and adverse clinical outcomes. Approximately 20% of patients undergoing PCI exhibit severe coronary calcification, which independently predicts incomplete revascularization, increased mortality, and higher rates of major adverse cardiovascular events over mid-term follow-up. Recent advances have focused on improving the assessment and management of calcified lesions. Intracoronary imaging modalities, including intravascular ultrasound and optical coherence tomography, allow precise detection and characterization of calcium burden, overcoming the limitations of angiography. These tools play a pivotal role in guiding procedural strategy, enabling tailored selection of calcium-modifying techniques based on lesion morphology, and optimizing stent deployment. Technological innovations have significantly expanded therapeutic options. While non-compliant balloon angioplasty alone is often insufficient, adjunctive devices such as cutting and scoring balloons improve plaque modification in focal disease. Atherectomy techniques, including rotational and orbital systems, are effective for more complex lesions but require technical expertise and carry procedural risks. Intravascular lithotripsy has emerged as a promising, less aggressive modality capable of fracturing deep calcium, while excimer laser atherectomy offers an alternative for resistant lesions. Despite these advances, current evidence supporting calcium-modifying strategies is largely based on procedural outcomes rather than definitive improvements in long-term clinical endpoints. Meta-analyses and randomized trials have not demonstrated clear superiority of any single technique, and most studies remain underpowered. Intriguingly, recent data suggest that outcomes in treated calcified lesions may approximate those of non-calcified disease, raising the hypothesis that these technologies could mitigate the adverse impact of calcification. However, this remains unproven, highlighting the urgent need for adequately powered randomized trials to determine their true clinical benefit. Full article