Search Results (34,163)

Background and Objectives: This study aimed to evaluate postoperative outcomes following transaxillary first rib resection with concomitant anterior scalenotomy (Roos procedure) for Thoracic Outlet Syndrome, using an ambispective design with a standardized two-year multimodal follow-up in a prospectively observed subgroup. Materials and Methods: This ambispective observational cohort study included 32 patients (87.5% women; mean age, 33.8 years) who underwent transaxillary first rib resection with anterior scalenotomy for Thoracic Outlet Syndrome. Of these, seven patients comprised the retrospective cohort, having undergone surgery between 2017 and 2019, while the remaining 25 patients were enrolled prospectively and underwent surgery from 2020 onwards. Patients were classified as having neurogenic, vascular (arterial or venous), or mixed Thoracic Outlet Syndrome. Retrospective data were obtained from medical records, while prospectively treated patients were followed according to a predefined postoperative protocol. Longitudinal changes in clinical outcomes were analyzed using mixed linear and logistic regression models. Results: All analyzed symptoms improved after surgery (p < 0.05), with a significant reduction in upper limb edema over time (OR = 0.44, p = 0.002). The prevalence of positive provocative tests decreased notably across all maneuvers postoperatively. Pathological color duplex ultrasound findings of the upper limb vessels resolved almost completely during follow-up. Patient-reported outcome measures (CBSQ, DASH, and BPI) demonstrated meaningful postoperative improvement with sustained benefits over time. Electrophysiological evaluation revealed notable improvement in median sensory and motor nerve conduction parameters. Conclusions: Transaxillary first rib resection with anterior scalenotomy appears to improve clinical, functional, and objective outcomes in patients with Thoracic Outlet Syndrome; however, findings should be interpreted with caution due to the ambispective design, small sample size, and cohort heterogeneity, and require confirmation in larger prospective studies. Full article

Background and Objectives: Outcomes after trauma are traditionally attributed to injury severity and acute physiologic derangement. However, host vulnerability at presentation—reflecting underlying physiologic and nutritional status—may also be associated with bleeding severity and transfusion requirements following acute injury. Whether such vulnerability contributes additional risk information beyond established factors remains incompletely understood. Materials and Methods: We conducted a retrospective cohort study of adult trauma patients using a single-center trauma registry. Host vulnerability was assessed using a composite score (CE; range 0–3) based on admission hypoalbuminemia (<3.5 g/dL), anemia (hemoglobin < 11 g/dL), and reduced renal function (estimated glomerular filtration rate < 60 mL/min/1.73 m²). Primary outcomes were any blood transfusion and massive transfusion, defined as transfusion of ≥10 units of packed red blood cells within 24 h of admission. Associations between CE score and transfusion outcomes were evaluated using univariable and multivariable logistic regression models adjusted for age, Injury Severity Score (ISS), admission lactate level, and systolic blood pressure (SBP). Results: Among 4105 trauma patients, transfusion requirements increased progressively with higher CE scores. Rates of any transfusion rose from 21.7% in patients with CE 0 to 78.6% in those with CE 3, while massive transfusion increased from 1.9% to 23.1% across the same categories. In multivariable analyses, each 1-point increase in CE score was independently associated with higher odds of any transfusion (adjusted odds ratio [aOR] 3.21, 95% confidence interval [CI] 2.80–3.68) and massive transfusion (aOR 1.73, 95% CI 1.45–2.07). Conclusions: A composite score reflecting host vulnerability at presentation was associated with bleeding severity and transfusion requirements after trauma, beyond injury severity and acute physiologic factors. These findings suggest that simple laboratory-based markers may provide additional information for early risk stratification of hemorrhagic outcomes after trauma. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Objective: This study aimed to develop a nomogram prediction model for predicting 14-day in-hospital mortality in patients with acute myocardial infarction (AMI) and ventricular septal rupture (VSR). Methods: Clinical data of 86 hospitalized patients (44 survivors and 42 non-survivors within 14 days) were retrospectively collected in Nanjing First Hospital from 1 March 2015 to 7 August 2025. Lasso regression and multivariable logistic regression were used to identify predictors, which were subsequently incorporated into the nomogram development. The model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curves, with internal validation via 1000 bootstrap resamples. Results: Analysis of lasso regression and multivariable logistic regression analysis identified WBC count (OR = 1.31, 95% CI: 1.01–1.28, p = 0.040), D-dimer level (OR = 1.18, 95% CI: 1.01–1.38, p = 0.043), early revascularization (OR = 0.22, 95% CI: 0.06–0.88, p = 0.032), ventilatory support (OR = 3.48, 95% CI: 1.07–11.29, p = 0.038), and infection (OR = 3.97, 95% CI: 1.02–15.42, p = 0.047) as independent predictors of 14-day mortality for patients. Based on the results, a prediction nomogram model was constructed. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.866 (95% CI: 0.785–0.946), with sensitivity of 0.857 (95% CI: 0.751–0.963) and specificity of 0.818 (95% CI: 0.704–0.932). Calibration plots demonstrated acceptable agreement between predicted and observed probabilities; decision curve analysis (DCA) and clinical impact curve further confirmed its net benefit and clinical utility. By 1000 bootstrap resampling iterations, the model demonstrated an apparent AUC of 0.864, 95% CI: 0.776–0.938, confirming reasonable discriminative performance. Conclusions: In summary, this study developed a clinical interpretable nomogram to estimate short-term (14-day) in-hospital mortality risk in patients with AMI-VSR; it provides a robust and interpretable tool for predicting short-term in-hospital mortality. Full article

Purpose: To compare the efficacy of Superb Microvascular Imaging (SMI) with grayscale ultrasound (US) and dynamic contrast-enhanced MRI in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in invasive breast cancer. Methods: A total of 115 patients included in the study were evaluated based on their pre-treatment imaging findings (US, mammography, and MRI). Following completion of NAC, all patients underwent grayscale US and SMI examinations. In patients with available post-NAC MRI, treatment response was additionally assessed by comparing MRI findings. Imaging results were correlated with postoperative pathological outcomes, which served as the reference standard. pCR was defined as the absence of residual invasive carcinoma, regardless of ductal carcinoma in situ. Molecular subtype, Ki-67, and axillary status were recorded. Statistical analyses included chi-square tests and stepwise multiple logistic regression. Significance was set at p < 0.05 (95% CI). Results: The median age was 51 years (range: 30–75). Most tumors were high-grade (55%) and invasive ductal carcinoma (95%). Breast-pCR was achieved in 43% of patients. Significant predictors of pCR included hormone receptor negativity, HER-2 positivity, high Ki-67 expression (≥40%), non-luminal subtype, and complete radiologic response on US and MRI (p < 0.05). Lower SMI index values were strongly associated with pCR (p < 0.001), with an optimal cut-off of 1.8 demonstrating good diagnostic performance (AUC = 0.804, 95% CI: 0.721–0.887). In multivariate analysis, the combined model including US, SMI, HER-2 status, and MRI showed the highest predictive performance (AUC = 0.890, 95% CI: 0.829–0.950), explaining 55.1% of the variance in pCR. Conclusions: An SMI index < 1.8, HER-2 positivity, and complete response on US and MRI are independent predictors of pCR after NAC. Combining SMI with multimodal imaging significantly improves predictive accuracy. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

Background and Objectives: Acute ischemic stroke (AIS) caused by large vessel occlusion (LVO) remains a major cause of disability and mortality. Mechanical thrombectomy (MT) improves outcomes, but recovery varies. This study assessed the prognostic value of hypoperfusion intensity ratio (HIR), collateral circulation, and other clinical/imaging factors. Materials and Methods: This retrospective cohort study included 96 LVO patients treated with MT with or without intravenous thrombolysis (IVT) between 2020 and 2024 at a tertiary hospital. Inclusion required multimodal CT (CT, CTA, CTP) and clinical data (NIHSS, mRS). HIR, core volume, CBV index, mismatch ratio, and collateral status were evaluated using artificial intelligence (AI)-based software. Univariate/multivariate logistic regression identified predictors of poor outcome (mRS > 3 at 90 days). Results: Lower HIR (<0.5) and good collaterals were associated with favourable outcomes (p < 0.001). Multivariate analysis identified HIR, initial NIHSS, and procedure duration as independent predictors of poor outcome. CTP-derived core volume, cerebral blood volume index, and mismatch ratio were also significant predictors. ROC analysis showed the highest AUC for core volume (0.810). Diabetes mellitus was associated with a worse prognosis compared to other clinical factors. Conclusions: HIR and collateral status are independent predictors of functional recovery after MT. CTP-derived core volume and CBV index have strong prognostic value. AI-based perfusion analysis supports patient selection and risk stratification. Full article

Background/Objectives: Knowledge of plant-based diets is gaining increasing significance in adolescents due to the growing popularity of vegetarian and vegan dietary patterns. To date, there has been limited research examining the level of awareness and understanding of these diets among secondary school students, as well as the factors influencing their knowledge. The aim of the study was to determine the prevalence of plant-based diets and to assess knowledge regarding these dietary patterns among high school students, as well as to identify factors associated with both diet adherence and achieving sufficient nutritional knowledge. Methods: A cross-sectional study was conducted among 341 high school students. Data were collected using a self-administered paper questionnaire that included demographic information, self-reported body weight and height, adherence to plant-based diets, and knowledge of vegetarian and vegan nutrition. Nutritional knowledge was assessed using a structured 19-item questionnaire (25 scorable items) and verified for reliability (test–retest, Krippendorff’s alpha = 0.88). Based on a 25-point scale, a score of >60% (16–25 points) was categorized as ‘sufficient’ knowledge. Statistical analyses included the chi-square test, Mann–Whitney and Kruskal–Wallis non-parametric tests, and multivariable logistic regression to estimate adjusted odds ratios (aOR) for factors associated with sufficient knowledge. Results: The prevalence of plant-based diets in the study group was 16.1% (n = 55), with a significantly higher frequency observed among female students and those with sufficient nutritional knowledge. The majority of students (81.2%) achieved sufficient knowledge. Higher scores were observed among female students, those in higher grade levels, and those individuals adhering to plant-based diets (p < 0.05). Multivariate regression analysis indicated that male sex (aOR = 0.38 compared to females), higher grade level (aOR = 3.66 for grade 3 vs. grade 1; aOR = 3.62 for grade 4 vs. grade 1), residence in a rural area (aOR = 0.50), and non-adherence to a plant-based diet (aOR = 0.32) were independently associated with sufficient knowledge. Conclusions: The majority of high school students demonstrate sufficient knowledge regarding plant-based diets, with significant variations associated with sex, grade level, place of residence, and experience with plant-based diets. These findings underscore the need for targeted educational interventions, particularly among male students, those in lower grade levels, and individuals residing in rural areas. Full article

Background: Cardiovascular disease remains a leading cause of morbidity and mortality after kidney transplantation. The relative contribution of metabolic abnormalities and inflammatory burden to cardiac remodeling and subsequent clinical outcomes in kidney transplant recipients (KTRs) remains incompletely understood. Methods: In this retrospective cohort study, 152 KTRs underwent comprehensive cardiovascular evaluation at a stable post-transplant time point (12 ± 4 months after transplantation). Metabolic phenotype was assessed using metabolic syndrome and indices of insulin resistance and atherogenic dyslipidemia (TyG index, TG/HDL ratio, and atherogenic index of plasma [AIP]). Inflammatory status was evaluated using hs-CRP and complete blood count-derived indices. Echocardiographic damage composite (EDC) was defined as the presence of left ventricular hypertrophy, diastolic dysfunction, or left atrial enlargement. Patients were followed for major adverse clinical outcome (MACO), defined as cardiovascular event, graft failure, or death, and major adverse cardiovascular and cerebrovascular events (MACCE). Results: At baseline, 78 patients (51.3%) met criteria for EDC. EDC was strongly associated with higher TyG, AIP, TG/HDL, LDL/HDL ratio, and metabolic syndrome, whereas inflammatory markers showed no association. In multivariable logistic regression adjusted for age, sex, eGFR, and proteinuria, TyG remained independently associated with EDC (OR 1.13 per 0.1 increase, 95% CI 1.05–1.21; p = 0.001), independent of hs-CRP. Similar results were observed when AIP was evaluated in place of TyG (OR 10.39, 95% CI 2.22–48.71; p = 0.003). During follow-up, 78 patients developed MACO and 49 developed MACCE. In Cox regression analysis, graft dysfunction and inflammatory markers independently predicted MACO, whereas TyG was no longer significant. In contrast, TyG remained an independent predictor of MACCE after adjustment for confounders and inflammatory markers (HR 1.10 per 0.1 increase, 95% CI 1.04–1.16; p < 0.001). Similar results were observed when AIP was tested in place of TyG (HR 10.8, 95% CI 3.06–38.11; p < 0.001). Echocardiographic damage did not independently predict outcomes after adjustment. Conclusions: In KTRs, metabolic abnormalities reflecting insulin resistance and atherogenic dyslipidemia are closely associated with cardiac remodeling one year after transplantation and remain specifically linked to subsequent cardiovascular events. In contrast, systemic inflammation and graft dysfunction are the primary determinants of overall adverse clinical outcomes. Simple metabolic indices such as TyG and AIP may provide practical tools for cardiovascular risk stratification in this population. In Cox proportional hazards models, TyG (HR 1.102, 95% CI 1.043–1.164, p = 0.001) and AIP (HR 10.8, 95% CI 3.06–38.11, p < 0.001) were independently associated with cardiovascular events during follow-up, underscoring the role of atherogenic dyslipidemia in cardiovascular risk. Full article

The Consensual Assessment Technique (CAT) is a gold standard of creativity assessment which provides valid product-based creativity scores that are contextually grounded (stemming from raters with unique expertise, culturally and historically situated). However, its implementation is often demanding (raters’ burden, complex rating designs). This study investigates whether machine learning can effectively simulate expert-panel judgments of creativity using minimal training data. Using a dataset of 411 short stories, we compared the performance of Random Forest (RF), Gradient Boosted Trees, and Decision Tree models, based on story length and Divergent Semantic Integration, to predict expert CAT ratings by (1) identifying the optimal algorithm and (2) the minimum training sample size required for reliable prediction. Results indicate that RF consistently outperformed other algorithms, achieving high correlations with CAT scores (r = 0.80) using as few as 25 training stories. Furthermore, RF demonstrated superior accuracy and lower reliance on story length compared to LLM-based scoring models. These findings provide a robust proof-of-concept for using simulated expert panels as a scalable alternative to (decontextualized) automated assessment methods, while reducing human raters’ burden and the logistical constraints of complex rating designs. Extension of this work to different contexts, creativity tasks and domains are necessary to gauge its generalizability. Full article

Background: Renal dysfunction is common in patients hospitalized with acute decompensated heart failure (ADHF) and represents a key component of cardiorenal syndrome. However, the relationships between renal impairment, cardiorenal biomarkers, and echocardiographic markers of myocardial function remain incompletely characterized in ADHF populations. Methods: We conducted a cross-sectional analysis of 144 consecutive patients hospitalized with ADHF. Renal dysfunction was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Clinical, laboratory, and echocardiographic parameters were compared according to renal function. Correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed to evaluate associations between renal dysfunction, cardiorenal biomarkers, and myocardial functional indices. Results: Patients with renal dysfunction were older (p = 0.002) and more frequently had diabetes mellitus (p = 0.006). Echocardiographic evaluation demonstrated significantly lower systolic mitral annular velocity (S′) (p < 0.001) and higher E/e′ ratios (p < 0.001) in patients with renal dysfunction, whereas left ventricular ejection fraction (p = 0.133) and global longitudinal strain (GLS) (p = 0.121) were similar between groups. Log-transformed NT-proBNP and albuminuria were significantly correlated with S′, GLS, and E/e′ (all p < 0.001). In multivariable analysis adjusted for clinically relevant confounders, chronic kidney disease (OR 8.16, 95% CI 2.13–31.34; p = 0.002) and the E/e′ ratio (OR 2.01, 95% CI 1.52–2.66; p < 0.001) remained independently associated with renal dysfunction. ROC analysis showed that E/e′ had the strongest ability to distinguish between patients with and without renal dysfunction (AUC 0.887, 95% CI 0.834–0.941; p < 0.001). Conclusions: Renal dysfunction in ADHF is associated with echocardiographic markers reflecting impaired longitudinal myocardial function and elevated filling pressure, with E/e′ emerging as the strongest echocardiographic correlate. The integration of echocardiographic parameters with cardiorenal biomarkers may improve the characterization of the cardiorenal profile in patients hospitalized with ADHF. Full article

Logistics platforms (LPs) increasingly use multidimensional data to provide supply chain financing (SCF) to small and micro logistics enterprises (SMLEs). However, platform-centered data control can weaken financial institutions’ (FIs’) trust in platform data, thereby reducing the effectiveness of data-driven credit enhancement. To address this issue, this study integrates the social–ecological systems framework with evolutionary game theory and develops a tripartite evolutionary game involving FIs, LPs, and SMLEs. By comparing scenarios with and without regulatory governance, the study examines how regulatory governance affects the strategic evolution of data-driven credit enhancement in SCF for SMLEs. The results show that regulatory governance improves system performance through cost reduction, trust enhancement, and incentive alignment, thereby relaxing the conditions required for the system to evolve toward the Pareto-optimal state of credit granting, strict supervision, and non-default. The strategic choices of the three actors are mainly influenced by data acquisition costs, incentive intensity, and penalties. Numerical simulations further show that government incentives must exceed certain thresholds to promote cooperation, while penalty mechanisms play a critical role in constraining opportunistic behavior and accelerating convergence to the desirable equilibrium. These findings provide theoretical support and practical insights for improving data-driven credit enhancement in SCF for SMLEs. Full article

Background: Growth differentiation factor-15 (GDF-15) is a stress-responsive cytokine associated with inflammation, metabolic dysfunction, and cardiovascular disease. Its role as a biomarker of microvascular complications in type 2 diabetes (T2D) remains incompletely defined. Objective: To evaluate circulating GDF-15 levels and their association with microvascular complications in patients with T2D. Methods: This cross-sectional study included 160 participants divided into three groups: T2D (n = 93), obesity without carbohydrate disorders (n = 36), and healthy controls (n = 31). Microvascular complications (neuropathy, nephropathy, retinopathy) were assessed. Multivariable logistic regression and receiver operating characteristic (ROC) analysis were performed. Results: GDF-15 levels were significantly higher in T2D compared with non-diabetic individuals (267.5 ± 168.9 vs. 118.3 ± 55.5 pg/mL, p < 0.001). Higher GDF-15 was associated with neuropathy (odds ratio (OR) 1.985; 95% confidence interval (CI) 1.431–2.753) and nephropathy (OR 1.673; 95% CI 1.243–2.254) in unadjusted models. After adjustment, only nephropathy remained independently associated (OR 1.405; 95% CI 1.026–1.923). ROC analysis showed moderate discriminative ability for nephropathy (area under the curve (AUC) = 0.763). Conclusions: Circulating GDF-15 levels are significantly elevated in patients with T2D and are associated with microvascular complications, with the strongest independent association observed for diabetic nephropathy. These findings suggest that GDF-15 may represent a promising biomarker reflecting metabolic stress and risk of diabetic complications. Full article

This study investigates the feasibility of acoustic signal analysis for the assessment of weld bead quality in the shielded metal arc welding (SMAW) process. The work focuses on comparing time-domain acoustic signals and time–frequency spectrogram representations for the classification of welds as accepted or rejected according to standard welding inspection criteria. Two key acoustic descriptors, the fundamental frequency (F₀) and the harmonics-to-noise ratio (HNR), were extracted and analyzed to evaluate statistical differences between the two weld quality classes. Statistical tests, including Anderson–Darling, Levene, ANOVA, and Kruskal–Wallis (α = 0.05), revealed significant differences between accepted and rejected welds. Accepted welds exhibited a bimodal HNR distribution associated with transient arc instability at the beginning and end of the bead, whereas rejected welds showed more uniform acoustic behavior throughout the process. Subsequently, the acoustic data were represented using both audio signals and spectrograms and used as inputs for ten supervised machine learning models, including Support Vector Classifier (SVC), Logistic Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), and Naïve Bayes (NB). The results demonstrate that spectrogram-based representations significantly outperform time-domain signals, achieving accuracies of 0.95–0.96, ROC-AUC values above 0.95, and false positive and false negative rates below 6%. These findings indicate that, while scalar acoustic descriptors provide statistically significant insight into weld quality, time–frequency representations combined with machine learning enable a more robust and reliable framework for automated non-destructive evaluation, particularly in manual SMAW processes under realistic operating conditions. Full article

Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quantitative imaging information for risk stratification. This review synthesizes contemporary evidence on dose-optimized CT, advanced spectral technologies, and artificial intelligence (AI)-enabled analytics that are reshaping diagnosis, treatment selection, and triage. This review summarizes data supporting low-dose and ultra-low-dose CT protocols that preserve diagnostic accuracy while substantially reducing dose, and discusses how dual-energy CT, photon-counting CT, and radiomics facilitate noninvasive stone characterization and extraction of imaging biomarkers beyond size and location. It also reviews AI approaches for automated detection, segmentation, and volumetric quantification across CT, KUB, and ultrasounds, highlighting their potential to standardize stone-burden metrics. It further examines predictive models, including logistic regression, nomograms, and machine learning, for perioperative infectious complications, emergency department admission or intervention, procedure success, and long-term recurrence, and outlines reporting and validation frameworks and implementation considerations, including software as a medical device regulation and human oversight. In contrast to prior reviews that consider imaging and AI separately, this review integrates dose reduction, spectral characterization, and AI-driven analytics within real-world clinical pathways to distinguish established clinical applications from those that remain investigational. Integrating advanced CT and AI outputs into well-validated prediction models embedded in real-world workflows may enable safer imaging, more consistent triage, and more personalized follow-up for urinary stone disease. Full article