Search Results (6,942)

Background/Objective: Dental implant therapy requires clinicians to identify edentulous regions and adjacent teeth accurately to ensure precise and efficient implant placement. However, this process is time-consuming and subject to operator bias. To address this challenge, this study proposes an AI-assisted detection framework that integrates deep learning and image processing techniques to predict implant placement pathways on dental panoramic radiographs, supporting clinical decision-making. Methods: The proposed framework is first applied to YOLO models to detect edentulous regions and employs image enhancement techniques to improve image quality. Subsequently, YOLO-OBB is utilized to extract pixel-level positional information about neighboring healthy teeth. An implant pathway orientation visualization algorithm is applied to derive clinically relevant implant placement recommendations. Results: Experimental evaluation using YOLOv9m and YOLOv8n-OBB demonstrated stable performance in both recognition and accuracy. The models achieved Precision values of 88.86% and 89.82%, respectively, with an average angular error of only 1.537° compared to clinical implant pathways annotated by dentists. Conclusions: This study presents the first AI-assisted diagnostic framework for DPR-based implant pathway prediction. The results indicate strong consistency with clinical planning, confirming its potential to enhance diagnostic accuracy and provide reliable decision support in implant dentistry. Full article

Machine learning and deep learning techniques integrated with advanced sensing technologies have revolutionized agricultural engineering, addressing complex challenges in food production, quality assessment, and environmental monitoring. This survey presents a systematic review and meta-analysis of recent developments by examining the peer-reviewed literature from 2015 to 2024. The analysis reveals computational approaches ranging from traditional algorithms like support vector machines and random forests to deep learning architectures, including convolutional and recurrent neural networks. Deep learning models often demonstrate superior performance, showing 5–10% accuracy improvements over traditional methods and achieving 93–99% accuracy in image-based applications. Three primary application domains are identified: agricultural product quality assessment using hyperspectral imaging, crop and field management through precision optimization, and agricultural automation with machine vision systems. Dataset taxonomy shows spectral data predominating at 42.1%, followed by image data at 26.2%, indicating preference for non-destructive approaches. Current challenges include data limitations, model interpretability issues, and computational complexity. Future trends emphasize lightweight model development, ensemble learning, and expanding applications. This analysis provides a comprehensive understanding of current capabilities and future directions for machine learning in agricultural engineering, supporting the development of efficient and sustainable agricultural systems for global food security. Full article

Background: Standardised breast positioning and optimal compression are critical components of effective breast cancer screening. This scoping review aims to report the current landscape of automated software tools developed for image quality assessment and mammographic technique evaluation, and to examine their reported impact. Methods: A scoping review was undertaken across PubMed (MEDLINE), Scopus, and Emcare. Eligible studies were published between January 2014 and March 2025 and investigated the use of automated software or artificial intelligence-based tools to assess image quality, breast positioning, or compression in mammography or digital breast tomosynthesis. Results: Automated software was predominantly utilised in high-resource settings, where it provided benchmarked feedback, reduced the subjectivity inherent in traditional visual grading systems, and supported radiographer learning and skill development with measurable improvements. However, radiographer training in these systems, the impact of software on clinical workflow, and barriers to implementation, particularly in low-resource settings, were insufficiently addressed in the literature. Furthermore, no studies reported on the relationship between software-generated metrics and breast cancer screening outcomes. Conclusions: Automated software for image quality evaluation represents a significant advancement in breast screening, illustrating the potential of technology to strengthen the screening-to-treatment continuum in breast cancer care. Nonetheless, widespread adoption requires evidence that these tools directly contribute to improved cancer detection outcomes to justify their uptake. Full article

Hematopoietic stem cell (HSC) therapy remains essential in treating blood disorders, autoimmune diseases, neurodegenerative conditions, and cancers. Despite its potential, challenges arise from the inherent heterogeneity of HSCs and the complexity of their regulatory niche. Recent advancements in single-cell RNA sequencing and chromatin accessibility sequencing have provided deeper insights into HSC markers and chromatin dynamics, highlighting the intricate balance between intrinsic and extrinsic regulatory mechanisms. Zebrafish models have emerged as valuable tools in HSC research, particularly through live imaging and cellular barcoding techniques. These models have allowed us to describe critical interactions between HSCs and embryonic macrophages, involving reactive oxygen species and calreticulin signaling. These are essential for ensuring HSC quality and proper differentiation, with implications for improving HSC transplant outcomes. Furthermore, the review examines clonal hematopoiesis, with a focus on mutations in epigenetic regulators such as DNMT3A, TET2, and ASXL1, which elevate the risk of myelodysplastic syndromes and acute myeloid leukemia. Emerging technologies, including in vivo cellular barcoding and CRISPR-Cas9 gene editing, are being investigated to enhance clonal diversity and target specific mutations, offering potential strategies to mitigate these risks. Additionally, macrophages play a pivotal role in maintaining HSC clonality and ensuring niche localization. Interactions mediated by factors such as VCAM-1 and CXCL12/CXCR4 signaling are crucial for HSC homing and the stress response, opening new therapeutic avenues for enhancing HSC transplantation success and addressing clonal hematopoiesis. This review synthesizes findings from zebrafish models, cutting-edge sequencing technologies, and novel therapeutic strategies, offering a comprehensive framework for advancing HSC biology and improving clinical outcomes in stem cell therapy and the treatment of hematologic diseases. Full article

Extreme ultraviolet (EUV) lithography faces critical challenges in aberration control and patterning fidelity as technology nodes shrink below 3 nm. This work demonstrates how Source–Mask Optimization (SMO) simultaneously addresses both illumination and mask design to enhance pattern transfer accuracy and mitigate aberrations. Through a comprehensive optimization framework incorporating key process metrics, including critical dimension (CD), exposure latitude (EL), and mask error factor (MEF), we achieve significant improvements in imaging quality and process window for 40 nm minimum pitch patterns, representative of 2 nm node back-end-of-line (BEOL) requirements. Our analysis reveals that intelligent SMO implementation not only enables robust patterning solutions but also compensates for inherent EUV aberrations by balancing source characteristics with mask modifications. On average, our results show a 4.02% reduction in CD uniformity variation, concurrent with a 1.48% improvement in exposure latitude and a 5.45% reduction in MEF. The proposed methodology provides actionable insights for aberration-aware SMO strategies, offering a pathway to maintain lithographic performance as feature sizes continue to scale. These results underscore SMO’s indispensable role in advancing EUV lithography capabilities for next-generation semiconductor manufacturing. Full article

Background: Preperimetric glaucoma (PPG) is characterized by structural optic nerve damage without detectable functional impairment. Optical coherence tomography (OCT) is increasingly utilized to monitor glaucoma, though its reliability can be compromised by lens opacities. This study investigates retinal nerve fiber layer (RNFL) thickness changes after cataract surgery in patients with and without PPG, aiming to assess potential diagnostic inaccuracies due to cataract-induced imaging artifacts. Methods: Thirty eyes from 30 patients undergoing cataract surgery were analyzed, divided into two groups: Group 1 (n = 15) without glaucoma and Group 2 (n = 15) with PPG diagnosed using the Global Glaucoma Staging System. RNFL thickness was measured using Spectral-Domain OCT before and one month after phacoemulsification. Statistical analysis was performed using SPSS v23.0. Results: Postoperative RNFL thickness increased significantly in both groups, with a greater mean change in the PPG group (mean increase: 13 µm vs. 7 µm in controls; p < 0.00001). The greatest changes were observed in the inferior quadrants (p < 0.001). Image quality improved by approximately 34% post-surgery (p < 0.001). Despite higher postoperative RNFL values, none of the PPG eyes were reclassified as normal. Conclusions: In eyes with mild nuclear cataract, lens-related signal attenuation reduces absolute RNFL values but, in this cohort, had negligible impact on structural diagnostic classification. OCT-based structural findings in early glaucoma should therefore be interpreted with caution in the presence of cataract—recognizing that measurement bias may alter thickness values without changing PPG classification. Cataract surgery improves OCT reliability and can refine subsequent glaucoma assessment. Full article

Accurate detection of strip steel surface defects is a critical step to ensure product quality and prevent potential safety hazards. In practical inspection scenarios, defects on strip steel surfaces typically exhibit sparse distributions, diverse morphologies, and irregular shapes, while background regions dominate the images, exhibiting highly similar texture characteristics. These characteristics pose challenges for detection algorithms to efficiently and accurately localize and extract defect features. To address these challenges, this study proposes a Supervised Focused Feature Network for steel strip surface defect detection. Firstly, the network constructs a supervised range based on annotation information and introduces supervised convolution operations in the backbone network, limiting feature extraction within the supervised range to improve feature learning effectiveness. Secondly, a supervised deformable convolution layer is designed to achieve adaptive feature extraction within the supervised range, enhancing the detection capability for irregularly shaped defects. Finally, a supervised region proposal strategy is proposed to optimize the sample allocation process using the supervised range, improving the quality of candidate regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 81.2% on the NEU-DET dataset and 72.5% mAP on the GC10-DET dataset. Ablation studies confirm the contribution of each proposed module to feature extraction efficiency and detection accuracy. Results indicate that the proposed network effectively enhances the efficiency of sparse defect feature extraction and improves detection accuracy. Full article

Background/Objectives: Orthognathic surgery represents a surgical modality for the correction of craniofacial skeletal deformities. These procedures help achieve stable occlusion and improve facial symmetry, which in turn enhances functional outcomes and overall quality of life. However, to date, no consensus has been reached regarding whether orthognathic surgery also induces changes in the relationship of articular surfaces within the temporomandibular joints (TMJs). The primary objective of this study was to conduct a systematic review of research evaluating joint space dimensions based on CBCT imaging performed before and after orthognathic surgery. Methods: A comprehensive literature search was carried out across four electronic databases: PubMed, Web of Science, Cochrane Library, and Scopus. Two independent reviewers screened titles and abstracts according to predefined inclusion criteria. Eligible studies were subjected to critical appraisal, and relevant data were systematically extracted and summarized in tabular form. Results: Fourteen studies published between 2010 and 2024 met the inclusion criteria. In all studies, CBCT-based joint space measurements were conducted at least twice once preoperatively and once postoperatively, across a total of 527 patients included in the review. Conclusions: The synthesized evidence suggests that orthognathic surgery produces measurable modifications in the spatial relationship of TMJ articular surfaces. Nonetheless, the clinical relevance of these alterations appears to be modulated by several variables, including the surgical technique employed and the patient’s individual adaptive capacity. Full article

The relationship of diabetes mellitus (DM) with cardiovascular mortality and morbidity has been widely established. Diabetic cardiomyopathy (DBCM) has been increasingly recognized as the development of cardiac dysfunction accompanied by heart failure (HF) symptoms in the absence of obvious causes like coronary artery disease (CAD), hypertension (HTN) or valvular diseases. The objective of this review is to critically appraise the role of echocardiography in the diagnosis and prognostic stratification of DBCM. Echocardiography remains the first-line imaging modality due to its availability, repeatability, non-invasive nature and ability to assess structural and functional changes. Classical echocardiographic indices such as left ventricular hypertrophy and systolic and diastolic dysfunction assessment provide valuable information but they lack sensitivity, often remaining normal until advanced stages of DBCM. Recently developed echocardiographic modalities, including strain imaging, myocardial work indices and left atrial strain, may allow for earlier detection of subclinical myocardial dysfunction, having important prognostic implications. However, these advanced modalities require high imaging quality, expertise and standardization, being subject to technical and physio-logical limitations. Stress echocardiography, particularly exercise-based protocols, is an increasingly recognized, valuable tool for unmasking exertional abnormalities in filling pressures, myocardial reserve and pulmonary pressures that are not evident at rest. Until now, stress echocardiography requires validation in large cohorts to assess its prognostic power. This review highlights the importance of timely recognition of DBCM, underscores the advantages and disadvantages of current echocardiographic approaches and outlines future perspectives in multimodality imaging to improve patient outcomes. Full article

Few-Shot Semantic Segmentation (FSS) faces significant challenges in modeling complex backgrounds and maintaining prediction consistency due to limited training samples. Existing methods oversimplify backgrounds as single negative classes and rely solely on pixel-level alignments. To address these issues, we propose a symmetry-aware superpixel-enhanced FSS framework with a symmetric dual-branch architecture that explicitly models the superpixel region-graph in both the support and query branches. First, top–down cross-layer fusion injects low-level edge and texture cues into high-level semantics to build a more complete representation of complex backgrounds, improving foreground–background separability and boundary quality. Second, images are partitioned into superpixels and aggregated into “superpixel tokens” to construct a Region Adjacency Graph (RAG). Support-set prototypes are used to initialize query-pixel predictions, which are then projected into the superpixel space for cross-image prototype alignment with support superpixels. We further perform message passing/energy minimization on the RAG to enhance intra-region consistency and boundary adherence, and finally back-project the predictions to the pixel space. Lastly, by aggregating homogeneous semantic information, we construct robust foreground and background prototype representations, enhancing the model’s ability to perceive both seen and novel targets. Extensive experiments on the PASCAL-$5^i$ and COCO-${20}^i$ benchmarks demonstrate that our proposed model achieves superior segmentation performance over the baseline and remains competitive with existing FSS methods. Full article

At present, traditional X-ray inspection is used to inspect the welds of the bottom, barrel section and short shell parts of the launch vehicle, which has the disadvantages of low automation, complicated process and low efficiency, and cannot meet the fast-paced development needs of multiple models at present. Moreover, the degree of digitization is low, the test results are recorded in the form of negatives, data statistics, storage and access are difficult, and the circulation efficiency is low, which is not conducive to product quality control and traceability; At the same time, it cannot adapt to and meet the needs of digital and intelligent transformation and development. In this paper, a dual-robot collaborative digital radiographic inspection system for rocket tank welds is developed by combining dual-robot control technology and digital radiographic inspection technology. The system can be directly applied to digital radiographic inspection of tank bottom, barrel section and short shell welds of multiple types of launch vehicles; meanwhile, the dual-robot path planning technology based on the dual-mode is studied. Finally, the imaging software platform based on VS and Twincat3.0 VS2015 software combined with QT upper computer is designed. Experiments show that compared with the existing traditional ray detection methods, the detection efficiency of the system is improved by 5 times, the image sensitivity reaches W14, the resolution reaches D10, and the standardized signal-to-noise ratio reaches 128, which far exceeds the requirements of process technology A, and meets the current non-destructive detection work of multi-model rocket tank welds. Full article

With the continuous scaling-down of integrated circuit feature sizes, inverse lithography technology (ILT), as the most groundbreaking resolution enhancement technique (RET), has become crucial in advanced semiconductor manufacturing. By directly optimizing mask patterns through inverse computation rather than rule-based local corrections, ILT can more accurately approximate target design patterns while extending the process window. However, current mainstream ILT approaches—whether machine learning-based or gradient descent-based—all face the challenge of balancing mask optimization quality and computational time. Moreover, ILT often faces a trade-off between imaging fidelity and manufacturability; fidelity-prioritized optimization leads to explosive growth in mask complexity, whereas manufacturability constraints require compromising fidelity. To address these challenges, we propose an iterative deep learning-based ILT framework incorporating a lightweight model, ghost and adaptive attention U-net (EAAUnet) to accelerate runtime and reduce computational overhead while progressively improving mask quality through multiple iterations based on the pre-trained network model. Compared to recent state-of-the-art (SOTA) ILT solutions, our approach achieves up to a 39% improvement in mask quality metrics. Additionally, we introduce a mask constraint scheme to regulate complex SRAF (sub-resolution assist feature) patterns on the mask, effectively reducing manufacturing complexity. Full article

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article

In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article

Graphite nodularity is a key indicator for evaluating the microstructure quality of ductile iron and plays a crucial role in ensuring product quality and enhancing manufacturing efficiency. Existing research often only focuses on a single type of feature and fails to utilize multi-source information in a coordinated manner. Single-feature methods are difficult to comprehensively capture microstructures, which limits the accuracy and robustness of the model. This study proposes a hybrid estimation model for the graphite nodularity of ductile cast iron based on multi-source feature extraction. A comprehensive feature engineering pipeline was established, incorporating geometric, color, and texture features extracted via Hue-Saturation-Value color space (HSV) histograms, gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and multi-scale Gabor filters. Dimensionality reduction was performed using Principal Component Analysis (PCA) to mitigate redundancy. An improved watershed algorithm combined with intelligent filtering was used for accurate particle segmentation. Several machine learning algorithms, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), are applied to estimate graphite nodularity based on geometric features (GFs) and feature extraction. Experimental results demonstrate that the CatBoost model trained on fused features achieves high estimation accuracy and stability for geometric parameters, with R-squared (R²) exceeding 0.98. Furthermore, introducing geometric features into the fusion set enhances model generalization and suppresses overfitting. This framework offers an efficient and robust approach for intelligent analysis of metallographic images and provides valuable support for automated quality assessment in casting production. Full article