Search Results (680)

Background/Objectives: While intravenous thrombolysis (IVT) is the standard treatment for acute ischemic stroke (AIS) within 4.5 h of symptom onset, many patients present beyond this time window. Recent trials suggest that IVT may be both effective and safe in selected patients treated after the standard time window. Methods: We searched MEDLINE, Scopus, and ClinicalTrials.gov for randomized-controlled clinical trials (RCTs) and individual patient-data meta-analyses (IPDMs) of RCTs comparing IVT plus best medical treatment (BMT) to BMT alone in AIS patients who were last-known-well more than 4.5 h earlier. The primary efficacy outcome was a 90-day excellent functional outcome [modified Rankin Scale (mRS)-scores of 0–1]. Secondary efficacy outcomes included good functional outcome (mRS-scores 0–2) and reduced disability (≥1-point reduction across all mRS-strata). The primary safety outcome was symptomatic intracranial hemorrhage (sICH); secondary safety outcomes were any ICH and 3-month all-cause mortality. Subgroup analyses were performed stratified by different thrombolytics, time-windows, imaging modalities, and affected circulation. Results: Nine studies were included, comprising 1660 patients in the IVT-group and 1626 patients in the control-group. IVT significantly improved excellent functional outcome (RR = 1.24; 95%CI:1.14–1.34; I² = 0%) and good functional outcome (RR = 1.18; 95%CI:1.05–1.33; I² = 70%). IVT was associated with increased odds of reduced disability (common OR = 1.3; 95%CI:1.15–1.46; I² = 0%) and increased risk of sICH (RR = 2.75; 95%CI:1.49–5.05; I² = 0%). The rates of any ICH and all-cause mortality were similar between the two groups. No significant subgroup differences were documented. Conclusions: IVT in the extended time window improved functional outcomes without increasing mortality, despite a higher rate of sICH. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

This study analyzes artificial intelligence (AI)-based technologies for food waste reduction in restaurant management, particularly in the case of the Philippines. Using the multiple-stakeholder target-oriented robust-optimization (MS-TORO) approach, AI solutions are ranked based on cost, feasibility, infrastructure requirements, and effectiveness. The key findings highlight that Too Good To Go is the most practical AI solution due to its affordability and focus on surplus food redistribution, making it ideal for resource-limited settings. The study emphasizes the need for government support, financial incentives, and public–private partnerships to facilitate AI adoption. Additionally, integrating AI-driven waste reduction with food security initiatives and sustainability projects can enhance their impact. Addressing economic and infrastructural challenges is crucial for maximizing AI’s potential in food waste management in developing economies. Full article

One of the largest future applications of computer vision is in the healthcare industry. Computer vision tasks are generally implemented in diverse medical imaging scenarios, including detecting or classifying diseases, predicting potential disease progression, analyzing cancer data for advancing future research, and conducting genetic analysis for personalized medicine. However, a critical drawback of using Computer Vision (CV) approaches is their limited reliability and transparency. Clinicians and patients must comprehend the rationale behind predictions or results to ensure trust and ethical deployment in clinical settings. This demonstrates the adoption of the idea of Explainable Computer Vision (X-CV), which enhances vision-relative interpretability. Among various methodologies, attribution-based approaches are widely employed by researchers to explain medical imaging outputs by identifying influential features. This article solely aims to explore how attribution-based X-CV methods work in medical imaging, what they are good for in real-world use, and what their main limitations are. This study evaluates X-CV techniques by conducting a thorough review of relevant reports, peer-reviewed journals, and methodological approaches to obtain an adequate understanding of attribution-based approaches. It explores how these techniques tackle computational complexity issues, improve diagnostic accuracy and aid clinical decision-making processes. This article intends to present a path that generalizes the concept of trustworthiness towards AI-based healthcare solutions. Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

(1) Aim: To compare transverse dimensions measured on AI-generated intra-oral models and conventional digital intra-oral models. (2) Methods: A group of 38 patients treated with clear aligners was selected retrospectively from those whose records featured both AI-generated and conventional digital intra-oral models taken at the same timepoint. Transverse dimensions (inter-canine, inter-premolar, and inter-molar distances) on both upper and lower arches were evaluated and compared. Intra-class correlation index and paired t-test were applied to test the repeatability of measurements and statistically significant differences, respectively. Statistical significance was set at 0.05. (3) Results: Intra-class correlation index showed good repeatability. Paired t-test showed differences in measurements of the distances between the thicket area of gingiva on the palatal side of the upper first molar (p = 0.002), the gingival margin of the lower first molar (p = 0.014), and the mesio-vestibular cusps of the lower first molars (p = 0.019). (4) Conclusions: Transverse measurements were similar on AI-generated and conventional intra-oral .stl renderings. Statistical differences were found on posterior areas of both upper and lower dental arches, but are unlikely to be clinically significant. Full article

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

This study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP (SHapley Additive exPlanations), and counterfactual simulations to model and interpret the influence of ten parental involvement variables. The results identified time spent talking with parents, frequency of family meals, and encouragement to achieve good marks as the strongest predictors of reading performance. Counterfactual analysis revealed that increasing the time spent talking with parents and frequency of family meals from their minimum (1) to maximum (5) levels, while holding other variables constant at their medians, could increase the predicted reading score from the baseline of 358.93 to as high as 448.68, marking an improvement of nearly 90 points. These findings emphasize the educational value of culturally compatible parental behaviors. The study also contributes to methodological advancement by integrating interpretable machine learning with prescriptive insights, demonstrating the potential of XAI for educational policy and intervention design. Implications for educators, policymakers, and families highlight the importance of promoting high-impact family practices to support literacy development. The approach offers a replicable model for leveraging AI to understand and enhance student learning outcomes across diverse contexts. Full article

This research highlights the importance of Emotion Aware Technologies (EAT) and their implementation in serious games to assist children with Autism Spectrum Disorder (ASD) in developing social-emotional skills. As AI is gaining popularity, such tools can be used in mobile applications as invaluable teaching tools. In this paper, a new AI framework application is discussed that will help children with ASD develop efficient social-emotional skills. It uses the Jetpack Compose framework and Google Cloud Vision API as emotion-aware technology. The framework is developed with two main features designed to help children reflect on their emotions, internalise them, and train them how to express these emotions. Each activity is based on similar features from literature with enhanced functionalities. A diary feature allows children to take pictures of themselves, and the application categorises their facial expressions, saving the picture in the appropriate space. The three-level minigame consists of a series of prompts depicting a specific emotion that children have to match. The results of the framework offer a good starting point for similar applications to be developed further, especially by training custom models to be used with ML Kit. Full article

This position paper proposes a conceptual framework (CF-BIAI-SXT) for integrating blockchain with AI to enhance ethical governance, transparency, and privacy in high-risk AI applications that ensure societal resilience through the mitigation of sexual exploitation. Sextortion is a growing form of digital sexual exploitation, and the role of AI in its mitigation and the ethical issues that arise provide a good case for this paper. Through a combination of systematic and narrative literature reviews, the paper first explores the ethical shortcomings of existing AI systems in sextortion prevention and assesses the capacity of blockchain operations to mitigate these limitations. It then develops CF-BIAI-SXT, a framework operationalized through BPMN-modeled components and structured into a three-layer implementation strategy composed of technical enablement, governance alignment, and continuous oversight. The framework is then situated within real-world regulatory constraints, including GDPR and the EU AI Act. This position paper concludes that a resilient society needs ethical, privacy-first, and socially resilient digital infrastructures, and integrating two core technologies, such as AI and blockchain, creates a viable pathway towards this desideratum. Mitigating high-risk environments, such as sextortion, may be a fundamental first step in this pathway, with the potential expansion to other forms of online threats. Full article

This study explores the role of public theology in addressing contemporary societal challenges, emphasizing ethical dialogue in response to secularization, pluralism, technological transformation, and social and environmental issues. It situates pastoral theology in the Christian tradition as an active social practice aimed at promoting justice, equality, and the common good. The study highlights the emergence of public theology as a response to the participation of religious discourse in the public arena, considering communication and digital technology, and articulating theological reflection with real-world social issues. Additionally, it examines the profound significance of dialogue within religious discourse and stresses the importance of ethical reflection in technological advancements, particularly concerning AI (Artificial Intelligence). Moreover, Catholic social thought and the concept of integral ecology are analyzed in dialogue with the SDGs (Sustainable Development Goals), underlining the potential of public theology to promote socio-environmental justice through a holistic approach. Full article

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. Full article

Fiber-reinforced polymer (FRP) bars have great potential to replace steel bars in the design of reinforced concrete (RC) beams since they have numerous advantages such as high tensile strength and good corrosion resistance. Therefore, many studies including experiments and numerical simulations have focused on the behavior of FRP RC beams. In this paper, a comprehensive overview of previous studies is conducted to provide a thorough understanding about the behavior, the design, and the limitations of FRP RC beams. Particularly, experimental studies on FRP RC beams are collected and reviewed. In addition, the numerical analysis of FRP beams including the finite element (FE) analysis, the discrete element (DE) analysis, and artificial intelligence/machine learning (AI/ML) is summarized. Moreover, the international standards for the design of FRP RC beams are presented and evaluated. Through the review of previous studies, 93 tested specimens are collected. They can be a great source of reference for other studies. In addition, it has been found that the studies on the continuous beams and deep beams reinforced with FRP bars are still limited. In addition, more studies using DE analysis and AI/ML to analyze the response of FRP RC beams under loading conditions should be conducted. Full article

Automatic license plate recognition (ALPR) plays an important role in applications such as intelligent traffic systems, vehicle access control in specific areas, and law enforcement. The main novelty brought by the present research consists in the development of an automatic vehicle license plate recognition system adapted to the Romanian context, which integrates the YOLOv12 detection architecture with the PaddleOCR library while also providing functionalities for recognizing the type of vehicle on which the license plate is mounted and identifying the county of registration. The integration of these functionalities allows for an extension of the applicability range of the proposed solution, including for addressing issues related to restricting access for certain types of vehicles in specific areas, as well as monitoring vehicle traffic based on the county of registration. The dataset used in the study was manually collected and labeled using the makesense.ai platform and was made publicly available for future research. It includes 744 images of vehicles registered in Romania, captured in real traffic conditions (the training dataset being expanded by augmentation). The YOLOv12 model was trained to automatically detect license plates in images with vehicles, and then it was evaluated and validated using standard metrics such as precision, recall, F1 score, mAP@0.5, mAP@0.5:0.95, etc., proving very good performance. Experimental results demonstrate that YOLOv12 achieved superior performance compared to YOLOv11 for the analyzed issue. YOLOv12 outperforms YOLOv11 with a 2.3% increase in precision (from 97.4% to 99.6%) and a 1.1% improvement in F1 score (from 96.7% to 97.8%). Full article

Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (κ = 0.955 right, κ = 0.900 left; p < 0.001), with perfect agreement reported across all groups—including between the orthodontists themselves—for Angle classification (κ = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (p = 0.226) or overall Bolton values (p = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (p < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (≥0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use. Full article