Search Results (13,350)

Background and Objectives: Subclinical paroxysmal atrial fibrillation (AF) is often undetected by conventional screening strategies, until complications emerge. Artificial intelligence (AI) applied to sinus rhythm electrocardiograms has emerged as a promising tool to identify individuals with occult AF and to predict the risk of future incident AF. This scoping review synthesizes evidence from original studies evaluating AI models trained on sinus rhythm ECGs for AF detection or AF prediction. Materials and Methods: A comprehensive search of MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore was conducted to identify peer-reviewed studies from inception to November 2025. Eligible studies included original investigations in which the model input was a sinus rhythm ECG and the outcome was either paroxysmal AF or new-onset AF. Extracted variables included cohort characteristics, ECG acquisition parameters, AI architecture, model predictive performance, AF prediction horizon, clinical outcomes, and validation strategy. Risk of bias was assessed using PROBAST. Results: Nineteen studies met the inclusion criteria. Retrospective datasets ranging from several thousand to over one million ECGs and convolutional or deep neural network AI architectures were used in most studies. AI-ECG models demonstrated high diagnostic accuracy for detecting subclinical AF (ten studies; AUROC 0.75–0.90) and for predicting long-term new-onset AF (six studies; AUROC 0.69–0.85) from a single sinus rhythm ECG. Robust external validation was reported in eleven studies. Combining AI-ECG models with clinical risk factors improved AF predictive performance in several reports. Key limitations across studies included retrospective design, patient selection, limited calibration reporting, and sparse prospective impact data. Conclusions: AI-based analysis of sinus rhythm ECGs can detect occult AF and stratify future AF risk with moderate-to-high accuracy across multiple populations and healthcare systems. However, rigorous prospective trials, evaluating clinical benefit, cost-effectiveness, calibration across demographic groups, and real-world implementation, are required before broad adoption in clinical practice. Full article

This systematic literature review analyzes the role of dynamic difficulty adaptation (DDA) in serious games (SGs) to provide an overview of current trends and identify research gaps. The purpose of the study is to contextualize how DDA is being employed in SGs to enhance their learning outcomes, effectiveness, and game enjoyment. The review included studies published over the past five years that implemented specific DDA methods within SGs. Publications were identified through Google Scholar (searched up to 10 November 2025) and screened for relevance, resulting in 75 relevant papers. No formal risk-of-bias assessment was conducted. These studies were analyzed by publication year, source, application domain, DDA type, and effectiveness. The results indicate a growing interest in adaptive SGs across domains, including rehabilitation and education, with DDA methods ranging from rule-based (e.g., fuzzy logic) and player modeling (using performance, physiological, or emotional metrics) to various machine learning techniques (reinforcement learning, genetic algorithms, neural networks). Newly emerging trends, such as the integration of generative artificial intelligence for DDA, were also identified. Evidence suggests that DDA can enhance learning outcomes and game experience, although study differences, limited evaluation metrics, and unexplored opportunities for adaptive SGs highlight the need for further research. Full article

Background: Acquired brain injury (ABI) affects manual dexterity (MD) and cognitive functions, limiting daily activity performance. Occupational therapy aims to improve functionality and quality of life. Objective: To examine and describe the available evidence on the impact of MD training on cognitive processes and functional performance in adults with stroke, as well as to identify the most commonly used assessment tools and intervention techniques. Methods: Scoping review. A systematic literature search was conducted in PubMed and Scopus to identify experimental studies from the last 10 years involving adults with ABI who participated in interventions targeting upper-limb, MD, and cognitive function. A three-phase screening was carried out by two authors with duplicates removed using Zotero version 7.0. Results: Ten articles published between 2016 and 2023 were included. The most frequent interventions involved robotics and virtual reality. Eight studies were conducted by occupational therapists or included occupational therapy involvement, while two were conducted by physiotherapists. Training MD and upper-limb motor skills led to improvements in attention, memory, and executive functions. Conclusions: Findings support combined motor–cognitive interventions carried out by occupational therapists or physiotherapists to optimize rehabilitation outcomes, although further research is needed to strengthen the evidence. Full article

Background/Objectives: Vestibular migraine (VM) is one of the most prevalent causes of episodic vertigo, yet it remains underdiagnosed due to overlapping features with other vestibular disorders and the absence of definitive diagnostic tests. Vestibular evoked myogenic potentials (VEMPs) assess otolith and vestibular nerve function and may help identify pathophysiological mechanisms in VM. This systematic review aimed to evaluate the usefulness of VEMP in understanding VM, synthesize existing findings, and explore its clinical implications. Method: A systematic search was performed in PubMed, ProQuest, Scopus, Web of Science, and EMBASE up to 2025 following PRISMA guidelines. Studies were included if they assessed cVEMP and/or oVEMP in patients diagnosed with VM using established clinical criteria. Data extraction and quality assessment were conducted independently by three reviewers using Cochrane and Joanna Briggs Institute tools. A total of 2578 titles and abstracts were screened, and 28 studies met the inclusion criteria. Results: Across 28 studies, 23 reported VEMP abnormalities in VM. The most frequent findings were reduced amplitudes and increased asymmetry ratios compared to healthy controls, indicating potential otolithic dysfunction. Latency prolongations were less consistently reported. Differences between cVEMP and oVEMP findings in individuals with VM suggested variable involvement of saccular and utricular pathways, with oVEMP abnormalities appearing more prominent. Conclusions: VEMP testing reveals subtle vestibular dysfunction in VM, primarily reflected in reduced amplitude and altered asymmetry ratios. However, the association between VEMP abnormality and VM is inconclusive, specifically due to heterogeneity among the included studies. Although findings support its potential as a diagnostic adjunct, methodological variability (including variability in patient recruitment) underscores the need for standardized VEMP protocols to enhance diagnostic accuracy and comparability across studies. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

Background/Objectives: Emotional dysregulation (ED) is emerging as a major contributor to functional impairment in Autism Spectrum Disorder (ASD). Although effective behavioral interventions exist, pharmacological treatments remain constrained by side effects and variable tolerability. Given their neurobiological roles that include neurotransmission, inflammation, and neuroplasticity, vitamin D and omega-3 polyunsaturated fatty acids (PUFAs) have been identified as promising candidates for modulating emotional and behavioral dysregulation. This systematic review aimed to evaluate the efficacy of combined vitamin D and omega-3 supplementation in improving emotional and behavioral regulation in individuals with ASD. Methods: This review was conducted in accordance with PRISMA guidelines. Included studies were English peer-reviewed studies involving participants with ASD that assessed combined vitamin D and omega-3 suppleupplementation with outcomes related to emotional or behavioral dysregulation. The search was restricted to 2015–2025 to ensure inclusion of recent, methodologically consistent studies and to minimize heterogeneity in diagnostic criteria and supplementation protocols. Results: Of 649 records initially screened, 3 studies met inclusion criteria: one randomized controlled trial, one observational study, and one case report, involving participants ranging from early childhood to young adulthood. Across studies, combined supplementation was associated with improvements in irritability, hyperactivity, agitation, and self-injurious behaviors. These clinical effects were accompanied by specific biochemical changes, including reductions in the AA/EPA ratio, increases in serum 25(OH)D and omega-3 indices, and decreased urinary levels of HVA and VMA. Conclusions: This review indicates that co-supplementation with vitamin D and omega-3 fatty acids may exert preliminary beneficial effects on emotional and behavioral dysregulation in individuals with ASD, potentially through anti-inflammatory and neuroregulatory mechanisms. However, the available evidence remains limited due to a small number of studies, their modest sample size, and methodological heterogeneity. Further, biomarker-driven randomized studies are needed to confirm efficacy and delineate optimal dosing strategies for application in clinics. Full article

Background/Objectives: Low back pain (LBP) is increasingly prevalent among children and adolescents and represents a growing public health concern due to its potential persistence into adulthood. Screen-based sedentary behavior has substantially increased in pediatric populations. However, evidence regarding its association with LBP remains inconsistent, and the existence of a dose–response relationship is not well established. Methods: A systematic review and meta-analysis of observational studies was conducted in accordance with PRISMA guidelines. Studies examining the association between screen-based sedentary behavior and LBP in children and adolescents aged 6–18 years were included. Random-effects meta-analyses were used to pool continuous exposure estimates, and a multivariate random-effects dose–response meta-analysis was performed to assess changes in LBP risk across increasing levels of daily screen time. Results: A total of 30 studies were included. The pairwise meta-analysis of continuous exposure showed no statistically significant association between screen time and LBP, with OR = 1.02 (95% CI 0.65 to 1.59). In contrast, the dose–response meta-analysis demonstrated a significant positive association, with a 26% (95% CI 8% to 48%) increase in the odds of LBP for each additional hour of daily screen time. High between-study heterogeneity was observed, and most studies relied on self-reported measures of screen exposure and LBP, which may have introduced recall and misclassification bias and warrants cautious interpretation of the findings. Conclusions: Higher levels of screen-based sedentary behavior were associated with an increased risk of LBP in children and adolescents when examined using a dose–response approach, whereas pairwise meta-analyses did not identify a significant association. Nevertheless, substantial between-study heterogeneity and high risk of bias limit causal inference and require cautious interpretation. Full article

Using additive manufacturing for the design of inserts in injection moulding (IM) offers advantages in product development and customization. However, challenges related to operating temperature and mechanical resistance remain. This article presents a systematic screening methodology to evaluate the suitability of materials for specific applications. Ten commercial Material Extrusion (MEX) filaments were selected to produce test samples. Moldex3D simulation software was employed to model the IM process using two thermoplastics and to determine the temperature and pressure conditions that the printed inserts must withstand. Simulation results were critically interpreted and cross-referenced with the experimental material characterisations to evaluate material suitability. Nine of the ten MEX materials were suitable for IM with LDPE, and five with PP. Dimensional assessments revealed that six insert solutions required further post-processing for assembly, while three did not. All of the selected materials successfully survived 10 injection cycles without encountering any significant issues. The simulation results were validated by comparing temperature data from a thermal imaging camera during IM, revealing only minor deviations. The study concludes that combining targeted material characterization with CAE simulation provides an effective and low-cost strategy for selecting MEX filaments for injection moulding inserts, supporting rapid tooling applications in niche production. Full article

Background: Emotions—including joy, sadness, fear, and anger—are fundamental expressions of human experience. For children and adults who are deaf or hard-of-hearing, emotional experiences and communication can differ due to linguistic and communication-related factors. Methods: This scoping review identifies instruments that are suitable for assessing emotional intelligence in the context of the lived and cultural experiences of individuals who are deaf or hard-of-hearing. A comprehensive search was conducted in April 2024 following the JBI methodology. Results: Out of 3091 articles, 21 studies were included. Two adapted methods were identified: the Meadow/Kendall Social–Emotional Assessment Inventory and ISEAR-D. Assessments supported by sign language revealed no significant differences in age or gender. Conclusions: The authors recommend further development of screening instruments that reflect the specific experiences of the population who are deaf or hard-of-hearing. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA’s performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher’s Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher’s Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA’s potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation. Full article

Background and Objectives: Preeclampsia (PE) complicates 2–8% of pregnancies globally, with a higher incidence in developing countries. This condition poses significant risks to maternal and fetal health, contributing substantially to maternal and perinatal mortality, particularly in cases of early-onset PE, which is associated with severe complications. This review aims to synthesize current evidence regarding the predictive utility of ophthalmic artery Doppler for preeclampsia. Current strategies focus on early prediction and prevention to mitigate adverse outcomes and reduce the economic burden of hypertensive disorders in pregnancy. The International Federation of Gynecology and Obstetrics (FIGO) recommends first-trimester screening combining maternal risk factors, mean arterial pressure, serum placental growth factor (PlGF), and uterine artery pulsatility index (UtA-PI). High-risk women are advised to take low-dose aspirin (150 mg daily) until 36 weeks of gestation. Materials and Methods: This review explores an innovative predictive tool for PE: ophthalmic artery (OA) Doppler. Results: As a non-invasive and easily accessible method, OA Doppler provides valuable insights into intracranial vascular resistance, offering potential advantages in early risk assessment, particularly for preterm PE, the most severe form of the disease. Conclusions: Our findings suggest that OA Doppler may serve as a promising adjunct in PE screening, enhancing the early identification of high-risk pregnancies and improving clinical outcomes. Further research is warranted to validate its role in routine prenatal care. Full article

Aspergillus fumigatus, a saprophytic fungus, causes aspergillosis, primarily affecting the immunocompromised. The efficacy of triazole antifungals is compromised by resistance that has developed both clinically and environmentally. Widespread agricultural use of similar triazole fungicides selects for resistant genotypes, leading to potential food contamination and compromising treatment. This study assessed the presence of azole-resistant A. fumigatus in minimally processed food items commonly consumed in Brazil. A total of 25 commercial samples, including black pepper, yerba mate, and green coffee beans, were collected from different regions. Forty-two A. fumigatus isolates were recovered and screened for susceptibility to agricultural and clinical triazoles by determining EC₅₀ values for tebuconazole (0.04–0.7 µg/mL), itraconazole (0.06–0.5 µg/mL), and voriconazole (0.07–0.15 µg/mL). Sequence analysis of the CYP51A gene revealed the presence of M172V mutation, none of which are associated with resistance. Microsatellite genotyping indicated high genotypic diversity and genetic relatedness among isolates from different food sources. Although no azole-resistant phenotypes were identified, the consistent recovery of A. fumigatus from products not directly exposed to azole fungicides highlights the need for continued surveillance. Agricultural environments remain critical hotspots for the emergence and dissemination of resistance, reinforcing the importance of integrated One Health strategies in antifungal resistance monitoring. Full article

Background/Objectives: The iBreast Exam (iBE) electronically palpates the breast to identify possible abnormalities. The purpose of this study was to assess iBE feasibility and compare it to Clinical Breast Exam (CBE) for breast lesion detection. Methods: Prospective evaluation of 300 asymptomatic women, ≥18 years old, with CBE, iBE, and mammography was performed. Sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of iBE and CBE for detecting suspicious breast lesions were calculated using breast imaging as the reference standard. For women with one year follow up, the sensitivity, specificity, PPV, and NPV for cancer detection were calculated. Results: 300 women (mean age 58.9 years) underwent CBE, iBE, and mammography. In 2/300 (0.7%), CBE was positive; in 1/300 (0.3%), iBE was positive; and in 24/300 (8%), screening mammograms were positive. Nine had suspicious imaging findings with biopsy (three malignant and six benign). Of three cancers, all visualized mammographically, CBE and iBE detected an ipsilateral breast abnormality in one woman and missed two cancers (<2 cm). Sensitivity, specificity, NPV, and PPV of iBE and CBE were similar, with no statistically significant difference in NPV or PPV for detection of suspicious breast findings or breast cancer (p > 0.05). Conclusions: Mammography detected all breast cancers in our cohort and remains the standard of care. iBE is feasible to perform. Our pilot data demonstrates iBE performed similarly to CBE by trained nurse practitioners. Given our small study population, further investigation is warranted into the potential use of iBE where trained healthcare practitioners are not readily available. Full article

Background/Objectives: Dementia represents a growing public health challenge. The WHO Global Action Plan on the Public Health Response to Dementia emphasizes early detection, risk reduction, and innovation as key priorities. Mild Behavioral Impairment (MBI), defined as the emergence of persistent neuropsychiatric symptoms in older individuals, represents a potential marker of early neurodegeneration and possible window for early intervention. This review explores the role of MBI in dementia prevention, mapping current evidence within the WHO Global Action Plan framework. Methods: A comprehensive search was performed in PubMed, Scopus, and the official WHO website, during 1 September 2025–10 November 2025, without time restrictions. Eligible sources included original clinical studies, reviews, and policy documents addressing MBI, dementia prevention, and public health. Data were thematically synthesized according to the seven objectives of WHO: (1) dementia as a public health priority, (2) dementia awareness and friendliness, (3) dementia risk reduction, (4) dementia diagnosis, treatment, care and support, (5) support for dementia carers, (6) information systems for dementia, and (7) dementia research and innovation. Results: Accumulating evidence indicates that MBI assessment can capture early behavioral manifestations of neurodegenerative and other forms of dementia, correlating with fluid, neuroimaging and genetic biomarkers. Integrating MBI screening through the easy-to-administer MBI Checklist (MBI-C) into clinical and community-based care, including telemedicine pathways and research, may enhance early identification and personalized interventions, enrich the pool for clinical trials, and facilitate research in biomarker and therapy. MBI-related research further supports its integration in remote digital monitoring and population-based prevention. Conclusions: Embedding MBI-informed screening and interventions into national dementia strategies aligns with WHO objectives for early, equitable and scalable prevention and brain health. Full article