Search Results (408)

Background/Objectives: Root canal treatment (RCT) is a common dental procedure performed to preserve teeth by removing infected or at-risk pulp tissue caused by caries, trauma, or other pulpal conditions. A successful outcome, among others, depends on accurate identification of the root canal anatomy, planning a suitable therapeutic strategy, and ensuring a bacteria-tight root canal filling. Despite advances in dental techniques, there remains limited integration of computational methods to support key stages of treatment. This review aims to provide a comprehensive overview of computational methods applied throughout the full workflow of RCT, examining their potential to support clinical decision-making, improve treatment planning and outcome assessment, and help bridge the interdisciplinary gap between dentistry and computational research. Methods: A comprehensive literature review was conducted to identify and analyze computational methods applied to different stages of RCT, including root canal segmentation, morphological analysis, treatment planning, quality evaluation, follow-up, and prognosis prediction. In addition, a taxonomy based on application was developed to categorize these methods based on their function within the treatment process. Insights from the authors’ own research experience were also incorporated to highlight implementation challenges and practical considerations. Results: The review identified a wide range of computational methods aimed at enhancing the consistency and efficiency of RCT. Key findings include the use of advanced image processing for segmentation, image analysis for diagnosis and treatment planning, machine learning for morphological classification, and predictive modeling for outcome estimation. While some methods demonstrate high sensitivity and specificity in diagnostic and planning tasks, many remain in experimental stages and lack clinical integration. There is also a noticeable absence of advanced computational techniques for micro-computed tomography and morphological analysis. Conclusions: Computational methods offer significant potential to improve decision-making and outcomes in RCT. However, greater focus on clinical translation and development of cross-modality methodology is needed. The proposed taxonomy provides a structured framework for organizing existing methods and identifying future research directions tailored to specific phases of treatment. This review serves as a resource for both dental professionals, computer scientists and researchers seeking to bridge the gap between clinical practice and computational innovation. Full article

This review article focuses first on the historical development of present understanding of gap junction channel architecture, one of its goals being to enlighten younger generations of scientists about the early steps of this field that begun over half a century ago. Early findings on gap junction architecture are reviewed as follows. The channels cross the membrane and project from the membrane surfaces; they are made of six subunits (hexamers) and show dimples on both ends, which represent inner and outer openings of the channel. Images of the central dimples on both channel ends (channel pores) seen in freeze-fracture replicas correspond to the electron-opaque spots visible in negatively stained sections and in isolated junctions. The channels are linked to each other extracellularly. Calmodulin (CaM) is a major accessory protein of gap junctions that is involved in channel gating and gap junction formation and is also likely to play a key role in determining different patterns of channel aggregation. Full article

Understanding soil variability supports improved land use and soil security. This study aimed to generate uniform geophysical classes by integrating data from three proximal geophysical sensors with synthetic soil and satellite images using machine learning, proposing a soil survey protocol. Geophysical data—natural gamma-ray emissions (eU, eTh, K⁴⁰), magnetic susceptibility (κ), and apparent electrical conductivity (ECa)—were collected in Piracicaba, Brazil, and clustered into homogeneous geophysical-isoparameter classes. These classes were modeled alongside Synthetic Soil Images (SYSIs), Sentinel-2 (0.45–2.29 μm), Landsat (0.43–12.51 μm) imagery, and morphometric variables. Empirical validation compared the resulting geophysical-isoparameter map with conventional pedological and lithological maps. The Support Vector Machine (SVM) algorithm exhibited the best classification performance. Results demonstrated that geophysical sensors quantitatively and qualitatively capture soil attributes linked to formation processes and types. The geophysical-isoparameter map correlated well with pedological and lithological patterns. The proposed protocol offers soil scientists a practical tool to delineate soil and lithological units using combined sensor data. Promoting collaboration among pedologists, pedometric mappers, and remote sensing experts, this approach presents a novel framework to enhance soil survey accuracy and efficiency. Full article

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption. Full article

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare. Full article

The crystallization behavior of polymers affects the structure of aggregated states, which influences the properties of materials. Atomic force microscopy (AFM) is a helpful characterization tool with high spatial resolution at the nanometer-to-micrometer scale and low-destruction imaging capabilities, making it an important means of studying polymer crystallography. This review is intended for scientists in polymer materials and physics, aiming to inspire how the rich applications of AFM can be harnessed to address fundamental scientific questions in polymer crystallization. This paper reviews recent advances in polymer crystallization characterization based on AFM, focusing on its applications in visualizing hierarchical polymer crystal structures (single crystals, spherulites, dendritic crystals, and shish kebab crystals), investigating crystallization kinetics (in situ monitoring of crystal growth), and analyzing structure–property relationships (structural changes under temperature and stress). Finally, we introduce the application of the latest AFM technology in addressing key issues in polymer crystallization, such as single-molecule force spectroscopy (SMFS) and atomic force microscopy–infrared spectroscopy (AFM-IR). As AFM technology advances toward higher precision, greater efficiency, and increased functionality, it is expected to deliver more exciting developments in the field of polymer crystallization. Full article

Unrolled networks have become prevalent in various computer vision and imaging tasks. Although they have demonstrated remarkable efficacy in solving specific computer vision and computational imaging tasks, their adaptation to other applications presents considerable challenges. This is primarily due to the multitude of design decisions that practitioners working on new applications must navigate, each potentially affecting the network’s overall performance. These decisions include selecting the optimization algorithm, defining the loss function, and determining the deep architecture, among others. Compounding the issue, evaluating each design choice requires time-consuming simulations to train, fine-tune the neural network, and optimize its performance. As a result, the process of exploring multiple options and identifying the optimal configuration becomes time-consuming and computationally demanding. The main objectives of this paper are (1) to unify some ideas and methodologies used in unrolled networks to reduce the number of design choices a user has to make, and (2) to report a comprehensive ablation study to discuss the impact of each of the choices involved in designing unrolled networks and present practical recommendations based on our findings. We anticipate that this study will help scientists and engineers to design unrolled networks for their applications and diagnose problems within their networks efficiently. Full article

Mind wandering is a mental activity that occupies up to 50% of our waking time. While scientists have now started to acknowledge and to study the creative potential of mind wandering for our imaginative skills, fiction has long recognised its value. This article focuses on the depiction of mind wandering in fiction, with examples ranging from Virginia Woolf’s The Waves to Ayumu Watanabe’s movie Children of the Sea. In particular, I focus on how images related to water are employed in this respect. It appears that water-related metaphors and imagery are particularly significant for the depiction of the interlacement between mind wandering and processes of creativity connected to fiction. This article argues that the notion of fictional world per se can be enriched and better conceptualised as a less “fixed” entity if pictured as a fluid, stream-like mental construct, shaped by imaginative engagement and mind wandering. Full article

Fossils are portals to the past, providing researchers with vital information about the evolution of life on Earth throughout the geological eras. The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years. The bibliographic survey revealed that destructive methods continue to play a significant role in scientific production related to this topic, particularly in studies on 3D morphologies, diagenesis, nutritional ecology, dating, elucidating dietary or habitat preferences, or understanding the physiology of extinct species. However, noninvasive tools, such as Raman spectroscopy, are rapidly rising, particularly when integrated with imaging techniques. As such, fossil research continues to advance even beyond the borders of our planet, exploring extraterrestrial samples in a quest to unlock the universal mystery of life. At the same time, the advent of advanced AI methods—particularly model chatbots that rival the capabilities of experienced scientists—has facilitated and enhanced data interpretation and classification. As fossil research evolves, upcoming technological advancements in spatial resolution, penetration depth, and detection sensitivity will integrate state-of-the-art spectroscopic tools. This will undoubtedly take fossil research to new heights, generating breakthroughs that optimize analysis while preserving invaluable specimens. Overall, the present study offers a holistic overview of analytical techniques through meta-analysis and bibliometric mapping, including a critical assessment of commonly used methods and offering a glimpse into the integration of machine learning and AI tools in fossil research. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

This study presents the development of a noise-monitoring system for the storage ring at the Siam Photon Source, designed to detect and classify noise patterns in real time using beam position monitor (BPM) data. Noise patterns were categorized into four classes: broad peak, multipeak, normal peak, and no beam. Two BPMs located at the multipole wiggler section, BPM-MPW1 and BPM-MPW2, were selected for detailed monitoring based on consistent noise trends observed across the ring. The dataset was organized in two complementary formats: two-dimensional (2D) images used for training and validating the models and one-dimensional (1D) CSV files containing the corresponding raw numerical signal data. Pre-trained deep learning and 1D convolutional neural network (CNN) models were employed to classify these patterns, achieving an overall classification accuracy of up to 99.83%. The system integrates with the EPICS control framework and archiver log data, enabling continuous data acquisition and long-term analyses. Visualization and monitoring features were developed using CS-Studio/Phoebus, providing both operators and beamline scientists with intuitive tools to track beam quality and investigate noise-related anomalies. This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize the synchrotron radiation performance for user experiments. Full article

During rapid-onset environmental catastrophes, scientists may not always have sufficient time to conduct proper environmental surveys in all representative areas. Although coral bleaching events can be predicted to a certain extent in some areas by tracking sea surface temperatures (SSTs), current models from NOAA’s Coral Reef Watch tend to underestimate severity of bleaching in the Indian Ocean, as was evident in March 2024 when corals began bleaching after only experiencing 1–2 degree-heating weeks. To characterize the impacts of this event, I conducted citizen science-style surveys at 22 sites along a 600-km stretch of the Kenyan coastline. Thereafter, I trained an artificial intelligence (AI) to extract coral abundance and bleaching data from 2300 coral reef images spanning 11–12 hectares of reef area to estimate both coral cover and bleaching prevalence. The AI’s accuracy was >80%, though it was prone to false-positive bleaching classifications. Bleaching severity varied significantly across sites, as well as over time, as seawater continued to warm over the duration of the study period; on average, over 75% of all reef-building scleractinians had bleached. Across the 22 sites, the mean healthy coral cover was only 7–8%, vs. >30% at sites in the same areas in the late 1990s. Whether these corals can recover, and then withstand such heatwaves in the future, will be known all too soon. Full article

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Assessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both classical image processing techniques (Canny, Sobel, and Laplacian) and a deep learning model (DexiNed). The methodology integrates interdisciplinary collaboration between conservation professionals and computer scientists, applying these algorithms to artworks affected by environmental damage, including flooding. Preprocessing and post-processing techniques were used to enhance detection accuracy and reduce noise. The results show that while traditional methods often yield higher precision and recall scores, they are also sensitive to texture and contrast variations. These findings suggest that automated edge detection can support conservation efforts by streamlining condition assessments and improving documentation. Full article