Search Results (1,178)

Background: Intra-abdominal pressure (IAP) has recently been recognized as a new vital sign in critically ill patients. Microwave reflectometry has been proposed as a potential approach for non-invasive IAP measurement. However, systematic investigation on how individual anatomical and geometric factors influence changes in the microwave reflection response of the abdominal compartment is limited. Complementary information regarding illumination frequency and specific absorption rate (SAR) also warrants consideration. Objective: This study aimed to advance the current knowledge on using microwave radar-based sensors in IAP monitoring by studying the most influencing factors. The penetration depth and spot size versus radiation frequency is studied as well. Information on energy deposition due to radio-frequency exposure is investigated too. Methods: Numerical simulations were performed using abdominal models adjusted to represent different IAP levels. Reflection signal features were analyzed in relation to IAP-induced changes, and SAR was calculated using human models. Subsequently, a radar sensor prototype was tested on a benchtop abdominal phantom. Lin’s concordance correlation analysis was used to evaluate absolute agreement between radar-estimated IAP and reference IAP. Additional statistical analyses assessed bias, precision, concordance, and risk levels. Results: Sagittal abdominal diameter was the dominant factor affecting the microwave reflection response. Reflection amplitude showed a periodic trend consistent with abdominal displacement corresponding to multiples of half-wavelength values of the applied electromagnetic waves. Numerical SAR simulations showed increasing SAR with frequency while remaining below the applicable exposure limits under the investigated conditions. The radar sensor showed a bias of 0.43 mmHg and a precision of 2.55 mmHg. Concordance analysis among the paired changes remaining after application of the predefined exclusion criteria showed agreement in the direction of IAP change. Conclusion: The present study should be considered a preliminary proof of concept. Clinically, the technology is currently more suitable for early warning and trend monitoring than for precise absolute IAP measurement, and it does not yet replace standard intravesical measurements. Its ability to support clinical decision-making, including guiding fluid therapy, requires prospective validation in patients. Full article

Non-invasive continuous glucose monitoring remains an important technological challenge in diabetes management. This study was revised as a literature-grounded simulation and methodological proof-of-concept rather than an experimental clinical validation. The analysis used 320 independent synthetic reference–NIR glucose pairs generated from physiological ranges and performance distributions reported in the cited literature, together with an illustrative 576-point, 48-h synthetic time series. Three sensor archetypes were informed by published optical systems. Descriptive agreement was evaluated using mean absolute relative difference (MARD), Pearson correlation, Bland–Altman analysis, and the supplied Clarke Error Grid (CEG) categories. Candidate physiological and environmental factors were examined using Pearson correlations with 95% confidence intervals, raw p-values, and Holm correction. The synthetic dataset produced an overall MARD of 10.38% (SD = 7.67%), r = 0.958 (R² = 0.918, p < 0.001), a Bland–Altman bias of 4.32 mg/dL, and 89.4%/10.3%/0.3% of records in CEG zones A/B/D, respectively. All candidate-factor correlations were small and non-significant after multiplicity correction; the largest was hematocrit (r = 0.090, 95% CI −0.019 to 0.198; Holm-adjusted p = 0.852). Differences in MARD among sensor archetypes were also non-significant (ANOVA p = 0.369; Kruskal–Wallis p = 0.543). Adaptive calibration is therefore presented as a literature-informed framework for future prospective validation, not as a performance improvement demonstrated by the present synthetic dataset. No FDA or ISO compliance or clinical applicability is claimed. Full article

Background/Objectives: Assessment of Internet Gaming Disorder (IGD) relies on retrospective self-reports and clinical interviews, which may be affected by recall and social desirability biases and may be insensitive to behavioral change. This study evaluated an artificial intelligence (AI)-enabled, privacy-preserving digital phenotyping framework for personalized IGD risk stratification under controlled simulation assumptions. Methods: A synthetic dataset of 1000 virtual user profiles was generated with a 20% elevated-risk prevalence and 5% balanced stochastic label noise. Four aggregated telemetry features were modeled: average session duration, sessions per week, Late-Night Index, and application-switching rate. Random Forest, Logistic Regression, and Gradient Boosting classifiers were evaluated using a stratified 80:20 hold-out split, five-fold cross-validation, playtime-only baselines, label-noise sensitivity analysis, and 200 synthetic realizations. Results: The primary Random Forest model achieved a balanced accuracy of 0.850, a sensitivity of 0.800, a specificity of 0.900, an area under the receiver operating characteristic curve (ROC-AUC) of 0.909, an average precision (AP) of 0.779, and a Brier score of 0.089. As an internal consistency check under the pre-specified synthetic signal structure, all-feature models showed higher performance than playtime-only baselines, and feature importance analyses recovered the encoded signal hierarchy. Performance declined with increasing label noise. Across 200 realizations, mean ROC-AUC values for the three all-feature models ranged from 0.888 to 0.904, with overlapping empirical 95% intervals. Conclusions: The framework demonstrates the methodological feasibility of transforming aggregated telemetry into interpretable risk signals while avoiding content-level monitoring. These findings are hypothesis-generating and do not establish clinical validity or diagnostic performance. Longitudinal validation in clinically characterized cohorts is required before deployment. Full article

Accurate assessment of liver function is essential in the management of chronic liver disease, hepatobiliary malignancies, and surgical planning. In recent years, hepatobiliary magnetic resonance imaging (MRI) using gadolinium-based contrast agents has evolved from a purely morphological technique into a quantitative functional imaging modality capable of evaluating hepatocyte uptake, biliary excretion, and regional liver function. Quantitative MRI-derived biomarkers, including relative liver enhancement, liver-to-spleen ratio, T1 relaxometry, and pharmacokinetic modeling, have demonstrated significant correlations with established liver function tests and postoperative outcomes. At the same time, the introduction of photon-counting computed tomography (PCCT) and K-edge imaging has opened new perspectives in quantitative imaging. Energy-resolved photon-counting technology has demonstrated gadolinium discrimination and material decomposition under selected experimental conditions, raising the hypothesis that aspects of hepatobiliary contrast distribution might eventually be investigated with computed tomography (CT). However, reliable K-edge detection and quantitative hepatobiliary gadolinium imaging after standard clinical administration have not yet been established in humans. Phantom, simulation, preclinical, and limited non-hepatobiliary human studies have investigated gadolinium K-edge imaging, but direct evidence for functional hepatobiliary PCCT in humans remains lacking. This review aims to provide a comprehensive overview of MRI-derived evaluation of liver function, with particular attention to the experimental evidence exploring whether selected MRI-derived concepts might ultimately be investigated using PCCT and K-edge imaging, highlighting current evidence, technical challenges, translational applications, and future perspectives in precision hepatobiliary diagnostics. Full article

Background: Biomechanical evidence concerning sport-specific physiotherapy in elite kayaking remains limited. Objective: This study aimed to evaluate joint range of motion (ROM), muscle activation, and bilateral footrest force before and after a six-week physiotherapy programme. Methods: Twenty-two elite male kayakers participated in a non-randomised prospective controlled cohort study (study group, n = 11; control group, n = 11). Three-dimensional motion analysis, surface electromyography, and footrest-force measurements were obtained during ergometer kayaking. Results: No between-group difference in ROM change was statistically significant (p ≥ 0.398). Only right pectoralis major activation showed a significant exploratory between-group difference in change (1.40%MVC, 95% CI 0.62–2.18; p = 0.002). Footrest force increased bilaterally within the study group (both p < 0.001) but not within the control group (right, p = 0.908; left, p = 0.815). Exploratory summary-statistic estimates favoured the study group for right and left force change (both p < 0.001). Mean bilateral footrest force correlated with mean bilateral trunk rotation in the pooled sample (Spearman’s ρ = 0.953, p < 0.001). Conclusions: The programme was associated with increased bilateral footrest force during simulated paddling. ROM and most EMG changes were descriptive. The non-randomised design, small sample, and exploratory analysis preclude causal conclusions; clinical and performance applications require prospective evaluation using direct outcomes. Full article

This systematic review synthesises computational models of language deficits in bilingual aphasia, focusing on code-switching and translation. Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study identified 42 publications (2010–2025). Of the three deficit types, lexical retrieval and naming deficit simulations are most mature, replicating naming errors and predicting cross-language generalisation. Code-switching and translation deficit modelling is extremely limited: only a single computational lesioning study has addressed both through mechanistic simulation. Machine learning methods show preliminary promise for predicting treatment outcomes but remain largely data-driven. Three key challenges emerge: (1) methodological fragmentation—heterogeneous model types, lesion implementations, and evaluation metrics, with no standardised quantitative validation; (2) an underdeveloped theoretical foundation, as most studies are confined to behavioural fitting rather than testing hypotheses on impaired language control via computational lesions; (3) limited clinical translation, with few predictive studies integrating longitudinal patient data, with approximately 70% of studies concentrated in North America/Western Europe and biased toward English–Spanish bilinguals, limiting theoretical generalisability. Future work should shift from behavioural fitting to explainable mechanistic simulation, develop predictive frameworks for personalised rehabilitation, and establish standardised protocols bridging computational modelling with clinical application. Full article

Rapid advances in nanomaterials, wearable biosensors, multimodal acquisition, and artificial intelligence have enabled increasingly integrated health-monitoring systems, yet their progression toward health digital twins remains unclear. We conducted a protocol-driven scoping review of original studies combining nano-enabled multianalyte or multimodal sensing, AI-supported analysis, and health applications. PubMed/MEDLINE, Scopus, Web of Science Core Collection, and IEEE Xplore were searched using a publication cutoff of 10 July 2026; platform execution was completed on 13 July 2026. Two reviewers independently screened 528 unique records and assessed 20 full-text reports. A 79-item charting form was jointly verified for 12 included studies. Nine studies reported reference-method or matrix-relevant analytical validation, nine included human-sample or on-body evidence, and six acquired longitudinal or continuous data. Under the author-proposed, corpus-specific functional classification, six systems were L0, five L1, and one L2; none of the 12 met the L3 or L4 functional criteria. No included study combined dynamic individual-state assimilation with prospective prediction or simulation, and none reported external-site validation or formal predictive uncertainty quantification. Because eligibility required nano-enablement, multiple analytes or channels, AI integration, and selected clinical domains, these findings do not estimate the prevalence or maturity of health digital twins in the wider literature. Progress requires longitudinal multimodal data, validated state updating, external generalization, confidence-aware AI, and prospective evaluation of governed feedback. Full article

The concept of ranked sparsity, originally introduced in the context of penalized regression, arises in modeling applications when an expected disparity exists in the quality of information between different feature sets. Its presence can cause traditional and modern model selection methods to fail because such procedures commonly presume “covariate equipoise”—that each potential parameter is equally worthy of entering into the final model. However, this presumption does not always hold, especially in the presence of derived variables or with highly disparate feature sets (i.e., multi-modal data). For instance, when all possible interactions are considered as candidate predictors, the sheer number of them grossly inflates the number of false discoveries, resulting in unnecessarily complex and difficult-to-interpret models with many (truly spurious) interactions. In this work, we motivate a ranked sparsity extension to the Bayesian Information Criterion (RBIC) that requires a stronger level of evidence in order to allow certain variables (e.g., interactions vs main effects and genetic vs clinical covariates) into a model. We compare the performance of RBIC relative to competing methods for selecting polynomials and interactions in a simulation study and in two applications, showing that stepwise selection guided by RBIC produces better-predicting, more transparent models (with fewer false interactions) compared to existing alternatives. Full article

Background: Emergency departments (EDs) operate under severe time pressure, diagnostic uncertainty, and resource constraints, making the consistent application of clinical protocols challenging. This scoping review aimed to map the barriers to protocol adherence and the strategies reported to support implementation in emergency care. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched for publications from January 2000 to June 2026. Following predefined eligibility criteria, 58 publications were included and charted according to clinician-related, guideline-related, patient-related, and organizational determinants, and we reported the implementation strategies. No formal design-specific risk-of-bias or certainty-of-evidence assessment was performed; therefore, the synthesis was intended to map the available evidence rather than establish the comparative effectiveness. Results: Commonly reported barriers included limited guideline knowledge and clinical experience, cognitive overload and occupational fatigue, poor guideline usability and workflow compatibility, patient communication difficulties and clinical complexity, overcrowding, staffing shortages, and limited organizational support. The reported strategies included education and simulation, audit and feedback, clinical decision support, workflow redesign, multidisciplinary collaboration, and leadership engagement. However, the heterogeneity in study designs, clinical settings, definitions of adherence, and reported outcomes precluded ranking these strategies or determining whether particular combinations were superior. Conclusions: Protocol adherence in EDs appears to be shaped by interacting clinician-related, guideline-related, patient-related, and organizational factors. The identified strategies may support implementation, but their relative and comparative effectiveness remains uncertain. Digital health and artificial intelligence should be considered priorities for prospective evaluation rather than established solutions. Full article

Nd laser posterior capsulotomy is the standard treatment for posterior capsule opacification following cataract surgery. Although generally considered safe, inadvertent laser impacts on the intraocular lens (IOL) optic may induce permanent surface defects that contribute to postoperative dysphotopsias and reduced visual quality. This experimental study investigated the optical consequences of Nd laser-induced damage on two commercially available hydrophobic acrylic IOLs, focusing on retinal light distribution and optical image quality. Two hydrophobic acrylic monofocal IOL models, the CT LUCIA (Carl Zeiss Meditec) and the AcrySof IQ (Alcon), were mounted on a customized experimental holder and exposed to standardized Nd laser applications consisting of 5, 10, or 15 laser shots. Laser interactions were documented using the PhysioGo.Lite laser platform combined with infrared thermal imaging. Untreated IOLs served as controls. Optical performance was subsequently evaluated using a standardized optical bench according to ISO recommendations. Point spread function (PSF) and modulation transfer function (MTF) analyses were performed to quantify retinal image quality, light scattering, and optical degradation. Retinal light distribution was assessed using a high-resolution projection screen simulating the retinal image. Laser exposure produced permanent focal defects on the anterior optical surface of both IOL models, resulting in measurable optical degradation. Even the lowest laser exposure (five shots) generated detectable alterations in light propagation, characterized by increased peripheral light scattering, enlargement of the PSF halo, reduced central peak intensity, and irregular light distribution across the simulated retinal plane. Descriptively, increasing numbers of laser impacts were associated with more pronounced optical disturbances, particularly in the AcrySof IQ samples. MTF analysis demonstrated a reduction in optical performance across multiple spatial frequencies, indicating deterioration of image contrast and resolving power. Although both hydrophobic acrylic IOL models exhibited optical alterations after laser exposure, descriptive differences in the magnitude and distribution of light scatter suggested a possible influence of material composition, refractive index, and surface microarchitecture. These observations should be considered preliminary because of the limited sample size and absence of inferential statistical analysis. Under the present experimental conditions, Nd:YAG laser-induced pitting was associated with measurable structural and optical alterations in two hydrophobic acrylic IOL models. Surface defects alter retinal light distribution, increase forward light scatter, and reduce optical quality, providing a possible optical mechanism that may contribute to postoperative dysphotopsias, although clinical visual symptoms were not directly evaluated in this study. These findings highlight the importance of meticulous laser focusing on the posterior capsule to minimize inadvertent IOL damage and preserve postoperative visual quality. Further investigations combining optical bench analyses with patient-reported visual outcomes are warranted to better define the clinical significance of laser-induced IOL pitting. Full article

Interval censoring commonly arises in clinical trials, screening studies, and longitudinal medical investigations in which event status is assessed only at scheduled examination times. Rank-based procedures provide flexible tools for comparing interval-censored (IC) event-time distributions, but inference is usually based on first-order normal approximations that may be inaccurate in small or moderately sized samples and under substantial censoring. We develop a saddlepoint approximation (SPA) to the conditional permutation distribution of a linear rank statistic derived from the proportional reversed-hazard model with IC data. Conditional on the observed group size, the permutation distribution is represented through a bivariate cumulant generating function, and Skovgaard’s approximation is used to obtain computationally efficient tail probabilities without exhaustive permutation enumeration. The finite-sample performance of the proposed method is evaluated under log-normal, Weibull, and Gompertz event-time distributions and under monitoring schemes producing predominantly left, interval, or right-censored observations. Monte Carlo (MC) permutation p-values based on ${10}^6$ random permutations are used as a numerical benchmark (not the exact permutation distribution). Across the evaluated simulation scenarios, the SPA generally produces p-values that are closer to the MC permutation benchmark than those obtained from the standard normal approximation (NA). Applications to lung tumor, HIV drug-resistance, and breast-cosmesis data illustrate the relevance of the method to biomedical event-time studies. The proposed approximation provides an accurate and computationally efficient approach to rank-based inference for IC medical data. Full article

Extracorporeal shock wave therapy (ESWT) is a well-known biophysical therapy that offers several beneficial effects, including a presumed increase in the growth of blood vessels. The chorioallantoic membrane (CAM) assay is a well-established in vivo model for studying angiogenesis. The aim of this study was to develop an experimental setup to apply ESWT to CAM in a three-step process and investigate the vascular response. First, we conducted virtual simulations of various shock-wave application setups to CAM and subsequently tested them in preliminary studies to evaluate their feasibility. In the final stage, we employed the most suitable protocol to investigate the angiogenic effects of shock wave therapy in practical applications. Our findings suggest that ESWT increases the number of vessels in CAM. In the final biological experiment (n = 16 CAM per group), ESWT increased vessel number by approximately 14.3% (p = 0.050) and branching points by approximately 13.5% (p = 0.070), while mean vessel thickness decreased by approximately 8.0% (p = 0.047); total vascular area per image remained unchanged (p = 0.760). These effects were concentrated in short-to-medium, thinner-caliber vessels rather than reflecting a uniform increase in vascularity, consistent with the emergence of a denser microvascular network. This new experimental setup paves the way for future research into the angiogenic effects of ESWT and provides a multifaceted model as an intermediate step between in vitro mechanistic studies and clinical translation. Full article

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments. Full article

Artificial intelligence (AI) is entering cardiac surgery through predictive modelling, multimodal imaging, perioperative monitoring, workflow automation, and emerging computer-vision applications. The most mature evidence concerns risk prediction before and after surgery. Even in this domain, however, systematic reviews show that improvements over conventional statistical models are often modest and that routine clinical implementation remains limited. In surgical education, simulation, automated video analysis, and objective performance metrics may expand opportunities for deliberate practice and provide feedback that is less dependent on individual observers. Most of this evidence comes from general, laparoscopic, urological, and robotic surgery rather than cardiac-specific training, and its transferability should not be assumed. The same technologies also create risks. Automation bias, cognitive off-loading, reduced exposure to failure management, and displacement of mentor–trainee interaction may weaken the independent judgement on which safe cardiac surgery depends. Opaque models, dataset shift, inequitable performance, and uncertain accountability add further clinical and ethical concerns. This narrative review examines the current and emerging roles of AI across the cardiac surgical pathway and in cardiothoracic training, while distinguishing demonstrated applications from plausible but unproven uses. We propose a human-in-command framework based on external validation, local performance testing, transparent intended use, preserved manual and crisis-management competencies, simulation of technology failure, faculty oversight, competency-based credentialing, and continuous audit. AI should be judged not by technical novelty alone but by whether it improves care while preserving the ability of surgeons and teams to operate safely when the technology is unavailable or wrong. Full article

Missing data remain a major challenge in breast cancer research because they can introduce bias, reduce statistical efficiency, and compromise the performance of predictive models. Although numerous imputation techniques have been proposed, their comparative performance under different missing-data mechanisms and their impact on downstream classification remain inadequately understood. This study systematically compared statistical and machine learning-based imputation methods using two publicly available breast cancer datasets representing complementary clinical settings. The methods were evaluated under simulated Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) mechanisms using both reconstruction accuracy and downstream classification performance. The results showed that no single imputation method consistently achieved the best performance across both datasets. Regularized regression and machine learning-based methods generally outperformed conventional statistical approaches, although the optimal method depended on the characteristics of the dataset. Furthermore, the best-performing imputation methods preserved downstream classification performance despite the introduction of missing data, demonstrating that reconstruction accuracy alone is insufficient for selecting imputation strategies intended for predictive modelling. Overall, the findings highlight the importance of considering dataset characteristics, missing-data mechanisms, and the intended analytical objective when selecting imputation methods. The proposed evaluation framework provides a robust approach for assessing missing-data handling strategies in breast cancer prediction studies and other biomedical machine learning applications. Full article