Search Results (345)

Cognitive impairment is a prevalent and disabling feature of multiple sclerosis (MS), significantly impacting patients’ quality of life (QoL) and psychological well-being. Despite its clinical relevance, there are currently no approved pharmacological treatments for cognitive deficits in MS, highlighting the need for effective non-pharmacological interventions. This narrative review explores evidence from studies evaluating the efficacy of cognitive re-education (CR) approaches (including traditional, group-based, computer-assisted, virtual reality, and innovative methods such as music therapy) on cognitive and QoL outcomes in people with MS. The findings demonstrate that while CR consistently influences cognitive domains such as memory, attention, and executive function, its effects on QoL are more variable and often depend on intervention type, duration, and individual patient characteristics. Notably, integrative approaches like virtual reality and music therapy show promising results in enhancing both cognitive performance and psychosocial well-being. Several studies report that cognitive gains are accompanied by improvements in mental health and functional QoL, particularly when interventions are tailored to individual needs and delivered within multidisciplinary frameworks. However, some interventions yield only limited or transient QoL benefits, underlining the importance of personalized, goal-oriented strategies that address both cognitive and psychosocial dimensions. Further research is needed to optimize intervention strategies and clarify the mechanisms linking cognitive and QoL outcomes. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

Narrative review synthesizes the most current literature on the SARS-CoV-2 XEC variant, focusing on its genomic evolution, immune evasion characteristics, epidemiological dynamics, and public health implications. To achieve this, we conducted a structured search of the literature of peer-reviewed articles, preprints, and official surveillance data from 2023 to early 2025, prioritizing virological, clinical, and immunological reports related to XEC and its parent lineages. Defined by the distinctive spike protein mutations, T22N and Q493E, XEC exhibits modest reductions in neutralization in vitro, although current evidence suggests that mRNA booster vaccines, including those targeting JN.1 and KP.2, retain cross-protective efficacy against symptomatic and severe disease. The XEC strain of SARS-CoV-2 has drawn particular attention due to its increasing prevalence in multiple regions and its potential to displace other Omicron subvariants, although direct evidence of enhanced replicative fitness is currently lacking. Preliminary analyses also indicated that glycosylation changes at the N-terminal domain enhance infectivity and immunological evasion, which is expected to underpin the increasing prevalence of XEC. The XEC variant, while still emerging, is marked by a unique recombination pattern and a set of spike protein mutations (T22N and Q493E) that collectively demonstrate increased immune evasion potential and epidemiological expansion across Europe and North America. Current evidence does not conclusively associate XEC with greater disease severity, although additional research is required to determine its clinical relevance. Key knowledge gaps include the precise role of recombination events in XEC evolution and the duration of cross-protective T-cell responses. New research priorities include genomic surveillance in undersampled regions, updated vaccine formulations against novel spike epitopes, and long-term longitudinal studies to monitor post-acute sequelae. These efforts can be augmented by computational modeling and the One Health approach, which combines human and veterinary sciences. Recent computational findings (GISAID, 2024) point to the potential of XEC for further mutations in under-surveilled reservoirs, enhancing containment challenges and risks. Addressing the potential risks associated with the XEC variant is expected to benefit from interdisciplinary coordination, particularly in regions where genomic surveillance indicates a measurable increase in prevalence. Full article

Introduction: Artificial intelligence (AI) is increasingly being applied to improve the diagnosis and management of inflammatory bowel disease (IBD). Aims and Methods: We conducted a narrative review of the literature on AI applications in IBD endoscopy, focusing on diagnosis, disease activity assessment, therapy prediction, and detection of dysplasia. Results: AI systems have demonstrated high accuracy in assessing endoscopic and histological disease activity in ulcerative colitis and Crohn’s disease, with performance comparable to expert clinicians. Machine learning models can predict response to biologics and risk of complications. AI-assisted technologies like confocal laser endomicroscopy enable real-time histological assessment. Computer-aided detection systems improve identification of dysplastic lesions during surveillance. Challenges remain, including need for larger datasets, external validation, and addressing potential biases. Conclusions: AI has significant potential to enhance IBD care by providing rapid, objective assessments of disease activity, predicting outcomes, and assisting in dysplasia surveillance. However, further validation in diverse populations and prospective studies are needed before widespread clinical implementation. With ongoing advances, AI is poised to become a valuable tool to support clinical decision-making and improve patient outcomes in IBD. Addressing methodological, regulatory, and cost barriers will be crucial for the successful integration of AI into routine IBD management. Full article

Background: Motion capture (MoCap) technologies have transformed athlete monitoring, yet athletic departments face complex decisions when selecting systems for multiple sports. Methods: We conducted a narrative review of peer-reviewed studies (2015–2025) examining optical marker-based, inertial measurement unit (IMU) systems, including Global Navigation Satellite System (GNSS)-integrated systems, and markerless computer vision systems. Studies were evaluated for validated accuracy metrics across indoor court, aquatic, and outdoor field environments. Results: Optical systems maintain sub-millimeter accuracy in controlled environments but face field limitations. IMU systems demonstrate an angular accuracy of 2–8° depending on movement complexity. Markerless systems show variable accuracy (sagittal: 3–15°, transverse: 3–57°). Environmental factors substantially impact system performance, with aquatic settings introducing an additional orientation error of 2° versus terrestrial applications. Outdoor environments challenge GNSS-based tracking (±0.3–3 m positional accuracy). Critical gaps include limited gender-specific validation and insufficient long-term reliability data. Conclusions: This review proposes a tiered implementation framework combining foundation-level team monitoring with specialized assessment tools. This evidence-based approach guides the selection of technology aligned with organizational priorities, sport-specific requirements, and resource constraints. Full article

The bakery industry is undergoing a profound digital transformation driven by the increasing need for enhanced energy efficiency, operational resilience, and a commitment to environmental sustainability. Digital Twin (DT) technology, recognized as a fundamental component of Industry 4.0, provides advanced capabilities for intelligent energy management across bakery operations. This paper utilizes a narrative and integrative review approach, conceptually integrating emerging developments in using DT with respect toenergy management in the baking industry, including real-time energy monitoring, predictive maintenance, dynamic optimization of production processes, and the seamless integration of renewable energy sources. The study underscores the transformative benefits of adopting DT technologies, such as improvements in energy utilization, greater equipment reliability, increased operational transparency, and stronger alignment with global sustainability objectives. It also critically examines the technical, organizational, and financial barriers limiting broader adoption, particularly among small and medium-sized enterprises (SMEs). Future research directions are identified, emphasizing the potential of artificial intelligence-driven DTs, the adoption of edge computing, the development of scalable and modular platforms, and the necessity of supportive policy frameworks. By integrating DT technologies, bakeries can shift from traditional reactive energy practices to proactive, data-driven strategies, paving the way for greater competitiveness, operational excellence, and a sustainable future. Full article

Evaluating the coherence of narrative sequences extracted from large document collections is crucial for applications in information retrieval and knowledge discovery. While mathematical coherence metrics based on embedding similarities provide objective measures, they require substantial computational resources and domain expertise to interpret. We propose using large language models (LLMs) as judges to evaluate narrative coherence, demonstrating that their assessments correlate with mathematical coherence metrics. Through experiments on two data sets—news articles about Cuban protests and scientific papers from visualization conferences—we show that the LLM judges achieve Pearson correlations up to 0.65 with mathematical coherence while maintaining high inter-rater reliability (ICC > 0.92). The simplest evaluation approach achieves a comparable performance to the more complex approaches, even outperforming them for focused data sets while achieving over 90% of their performance for the more diverse data sets while using less computational resources. Our findings indicate that LLM-as-a-judge approaches are effective as a proxy for mathematical coherence in the context of narrative extraction evaluation. Full article

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care. Full article

Optical coherence tomography (OCT) is a non-invasive imaging technique that provides high-resolution, real-time visualization of soft and hard periodontal tissues. It offers micrometer-level resolution (typically ~10–15 μm) and a scan depth ranging from approximately 0.5 to 2 mm, depending on tissue type and system configuration. The field of view generally spans a few millimeters, which is sufficient for imaging gingiva, sulcus, and superficial bone contours. Over the past two decades, its application in periodontology has gained increasing attention due to its ability to detect structural changes in gingival and alveolar tissues without the need for ionizing radiation. Various OCT modalities, including time-domain, Fourier-domain, and swept-source OCT, have been explored for periodontal assessment, offering valuable insights into tissue morphology, disease progression, and treatment outcomes. Recent innovations include the development of three-dimensional (3D) OCT imaging and OCT angiography (OCTA), enabling the volumetric visualization of periodontal structures and microvascular patterns in vivo. Compared to conventional imaging techniques, such as radiography and cone beam computed tomography (CBCT), OCT offers superior soft tissue contrast and the potential for dynamic in vivo monitoring of periodontal conditions. Recent advancements, including the integration of artificial intelligence (AI) and the development of portable OCT systems, have further expanded its diagnostic capabilities. However, challenges, such as limited penetration depth, high costs, and the need for standardized clinical protocols, must be addressed before widespread clinical implementation. This narrative review provides an updated overview of the principles, applications, and technological advancements of OCT in periodontology. The current limitations and future perspectives of this technology are also discussed, with a focus on its potential role in improving periodontal diagnostics and personalized treatment approaches. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

Background/Objectives: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains. Methods: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders. Results: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain–computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach. Conclusions: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain–computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users’ physiological and behavioral data to optimize support in daily tasks. Full article

Background: Teaching methodology has recently undergone significant evolution from traditional apprenticeship models as we adapt to ever-increasing rates of technological advancement. Big data, artificial intelligence, and machine learning are on the precipice of revolutionising all aspects of surgical practice, with far-reaching implications. Robotic platforms will increase in autonomy as machine learning rapidly becomes more sophisticated, and therefore training requirements will no longer slow innovation. Materials and Methods: A search of published studies discussing surgeon training and computer-enhanced simulation robotics and emerging technologies using MEDLINE, PubMed, EMBASE, Scopus, CRANE, CINAHL, and Web of Science was performed in January 2024. Online resources associated with proprietary technologies related to the subject matter were also utilised. Results: Following a review of 3209 articles, 91 of which were published, relevant articles on aspects of robotics-based computer-enhanced simulation, technologies, and education were included. Publications ranged from RCTs, cohort studies, meta-analysis, and systematic reviews. The content of eight medical technology-based websites was analysed and included in this review to ensure the most up-to-date information was analysed. Discussion: Surgeons should aim to be at the forefront of this revolution for the ultimate benefit of patients. Surgical exposure will no longer be due to incidental experiences. Rather, surgeons and trainees will have access to a complete database of simulated minimally invasive procedures, and procedural simulation certification will likely become a requisite from graduation to live operating to maintain rigorous patient safety standards. This review provides a comprehensive outline of the current and future status of surgical training in the robotic and digital era. Full article

Background/Objectives: Pulmonary lymphangitic carcinomatosis (PLC) is a rare, aggressive manifestation of metastatic cancer characterized by lymphatic infiltration of the lungs, typically indicating advanced disease and poor prognosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) imaging in assessing PLC. Results: Current evidence demonstrates that [18F]FDG PET/CT achieves high diagnostic accuracy, with sensitivity and specificity ranging from 86 to 97% and 84 to 100%, respectively, particularly when employing semiquantitative metrics such as peritumoral standardized uptake value (SUVmax) thresholds (e.g., ≥2.1). PET/CT surpasses high-resolution computed tomography (HRCT) in distinguishing PLC from mimics like pulmonary sarcoidosis by identifying distinct metabolic patterns: bronchovascular hypermetabolism in PLC versus subpleural nodular uptake in sarcoidosis. Prognostically, metabolic tumor burden (e.g., SUVmax × involved lobes) and novel cPLC classifications (localized to the ipsilateral or contralateral lung) independently predict progression-free survival. However, challenges persist, including non-specific tracer uptake in inflammatory conditions and variability in SUV measurements due to technical factors. Emerging digital PET/CT systems, with enhanced spatial resolution, may improve the detection of focal PLC and reduce false negatives. While [18F]FDG PET/CT is invaluable for whole-body staging, therapeutic monitoring and biopsy guidance, the standardization of protocols and multicenter validation of prognostic models are critical for clinical integration. Future research should explore novel tracers (e.g., PSMA for prostate cancer-related PLC) and machine learning approaches to refine diagnostic and prognostic accuracy. Conclusions: This review underscores the role and the transformative potential of [18F]FDG PET/CT in PLC management while advocating for rigorous standardization to maximize its clinical utility. Full article

Aviation safety analysis increasingly relies on extracting actionable insights from narrative incident reports to support risk identification and improve operational safety. Topic modeling techniques such as Probabilistic Latent Semantic Analysis (pLSA) and BERTopic offer automated methods to uncover latent themes in unstructured safety narratives. This study evaluates the effectiveness of each model in generating coherent, interpretable, and semantically meaningful topics for aviation safety practitioners and researchers. We assess model performance using both quantitative metrics (topic coherence scores) and qualitative evaluations of topic relevance. The findings show that while pLSA provides a solid probabilistic framework, BERTopic leveraging transformer-based embeddings and HDBSCAN clustering produces more nuanced, context-aware topic groupings, albeit with increased computational demands and tuning complexity. These results highlight the respective strengths and trade-offs of traditional versus modern topic modeling approaches in aviation safety analysis. This work advances the application of natural language processing (NLP) in aviation by demonstrating how topic modeling can support risk assessment, inform policy, and enhance safety outcomes. Full article

Digital applications for supporting health management often fail to achieve large-scale adoption. Costs related to purchasing, maintaining, and using medical or sensor devices, such as smartwatches, currently hinder uptake and sustained engagement, particularly in the prevention and monitoring of lifelong conditions. As an alternative, smartphone-based passive monitoring could provide a viable strategy for lifelong use, removing hardware-related costs and exploiting the synergies between mobile health (mHealth) and ambient assisted living (AAL). However, smartphone sensor toolkits are not designed for diagnostic purposes, and their quality varies depending on the model, maker, and generation. This narrative overview of recent reviews (narrative meta-review) on the current state of smartphone-based passive monitoring highlights the strengths, weaknesses, opportunities, and threats (SWOT analysis) of this approach, which pervasively encompasses digital health, mHealth, and AAL. The results are then consolidated into a newly defined concept of a mobile biomarker, that is, a general model of medical indices for diagnostic tasks that can be computed using smartphone sensors and capabilities. Full article