Search Results (171)

Background and Objectives: Peripheral arterial disease (PAD) is highly prevalent in patients with end-stage renal disease and is associated with adverse cardiovascular outcomes. Although the ankle–brachial index (ABI) is widely used to identify PAD, it may not fully reflect the complex vascular pathophysiology in patients undergoing peritoneal dialysis (PD). Antibodies against oxidized low-density lipoprotein (anti-oxLDL Ab) have been implicated in atherogenesis; however, their clinical relevance in PD populations remains unclear. Materials and Methods: In this cross-sectional investigation, 90 patients receiving maintenance PD were included. PAD was defined by an ABI below 0.90, and serum anti-oxLDL antibody concentrations were quantified using an enzyme-linked immunosorbent assay. Results: Patients with PAD were older (p = 0.006), had a higher prevalence of diabetes (p = 0.010), and exhibited higher levels of triglycerides (p = 0.008), fasting glucose (p < 0.001), and C-reactive protein (CRP, p < 0.001), but lower anti-oxLDL Ab levels (p = 0.008). Multivariable logistic regression demonstrated that reduced anti-oxLDL Ab levels (per 10 mU/mL increase, odds ratio [OR]: 0.803, 95% confidence interval [CI]: 0.648–0.995, p = 0.045) and increased CRP levels (per 0.1 mg/dL increase, OR: 1.662, 95% CI: 1.152–2.398, p = 0.007) were independently associated with PAD, with consistent results across penalized regression models. Log-transformed anti-oxLDL Ab levels were positively correlated with both left and right ABI values (p = 0.005 and p = 0.017, respectively). Decision curve analysis indicated that the anti-oxLDL Ab-based model provided greater net benefit compared with the treat-all and treat-none strategies across a range of threshold probabilities. Conclusions: Reduced serum anti-oxLDL Ab levels are independently associated with PAD in patients undergoing PD. Serum anti-oxLDL Ab levels are positively associated with ABI values. These findings suggest that impaired immunity against oxidized LDL may contribute to vascular disease in PD patients. Full article

Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from $O\left(n^2\right)$ to $O(log(n\left)\right)$ where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m² polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions. Full article

Background: The heterogeneous nature of cancer with varying degrees of fat, necrosis, fibrosis, and varying degrees of tissue repair severely impacts the success of acquiring adequate tissue samples during percutaneous image-guided biopsy. Although ultrasound or CT fluoroscopy are used to identify tumor location and thus to guide biopsy needle insertion, these technologies do not provide the necessary resolution to determine tissue composition and enable the selection of the most appropriate location for biopsy specimen extraction. As a result, biopsy must be repeated, leading to significant cost to the health care system. Methods: In this study, we introduce a combined optical imaging/artificial intelligence (OI/AI) methodology for the real-time assessment of tissue morphology at the tip of the biopsy needle, prior to the collection of a biopsy specimen. Addressing a significant clinical challenge, this approach aims to reduce the proportion of biopsy cores—currently as high as 40%—that yield low diagnostic value due to elevated adipose or low tumor content. Our methodology employs micron-scale optical coherence tomography (OCT) imaging to obtain detailed structural tissue information using a minimally invasive needle probe. The OCT images are automatically analyzed using a convolutional neural network (CNN)-driven AI software developed by our team. A U-net style architecture was used to segment regions of tumor from the OCT scans. U-Net is a specialized convolutional neural network (CNN) architecture designed for fast, precise image segmentation, which involves classifying each pixel in an image to outline objects. This streamlined approach shows promise to provide clinicians with real-time results, supporting more accurate and informed decisions regarding biopsy site selection. To evaluate this technology, we conducted a clinical study using a custom-made OCT imager and recorded OCT images from patients diagnosed with liver cancers. Expert OCT interpreters supplied annotated reference images that were used to train a custom AI algorithm. Results: OCT imaging with ~10 mm axial and 20 mm lateral resolution enabled the collection of high-quality images of the tissue. The AI analysis was performed offline. UNet achieved an AUC of ~0.877 on the validation dataset, indicating promising performance for the relatively small data set used to train the model. The AI model matched human interpretations approximately 90% of the time, highlighting its promise for making biopsy procedures both more accurate and more efficient. Conclusions: A novel OCT instrument and AI software were evaluated for assessing tissue composition at the tip of biopsy needle. The OCT instrument produced micron-scale resolution images of the tissue, enabling AI analysis and accurate real-time discrimination of tissue type. This preliminary study demonstrated the clinical potential of this technology for improving biopsy success. Full article

This study is grounded in the macro-context of smart agriculture and global food security. Due to population growth and climate change, precise and efficient monitoring of crop distribution and growth is vital for stable production and optimal resource use. Remote sensing combined with deep learning enables multi-scale agricultural monitoring from field identification to disease diagnosis. However, current models face three deployment bottlenecks: high complexity hinders operation on edge devices; scarce labeled data causes overfitting in small-sample cases; and there is insufficient generalization across regions, crops, and imaging conditions. These issues limit the large-scale adoption of intelligent agricultural technologies. To tackle them, this paper proposes a lightweight crop recognition model, MAF-RecNet. It aims to achieve high accuracy, efficiency, and strong generalization with limited data through structural optimization and attention mechanism fusion, offering a viable path for deployable intelligent monitoring systems. Built on a U-Net with a pre-trained ResNet18 backbone, MAF-RecNet integrates multiple attention mechanisms (Coordinate, External, Pyramid Split, and Efficient Channel Attention) into a hybrid attention module, improving multi-scale feature discrimination. On the Southern Hebei Farmland dataset, it achieves 87.57% mIoU and 95.42% mAP, outperforming models like SegNeXt and FastSAM, while maintaining a balance of efficiency (15.25 M parameters, 21.81 GFLOPs). The model also shows strong cross-task generalization, with mIoU scores of 80.56% (Wheat Health Status Dataset in Southern Hebei), 90.20% (Global Wheat Health Dataset), and 84.07% (Corn Health Status Dataset). Ablation studies confirm the contribution of the attention-enhanced skip connections and decoder. This study not only provides an efficient and lightweight solution for few-shot agricultural image recognition but also offers valuable insights into the design of generalizable models for complex farmland environments. It contributes to promoting the scalable and practical application of artificial intelligence technologies in precision agriculture. Full article

This study aims to identify an effective neural network architecture for the task of semantic segmentation of the surface of beer wort at the stage of primary fermentation, using deep learning methodologies. Four contemporary architectures were evaluated and contrasted. The following networks are presented in both baseline and optimized forms: U-Net, DeepLabV3+, Fast-SCNN, and SegNet. The models were trained on a dataset of images depicting real beer surfaces at the primary fermentation stage. This was followed by the validation of the models using key metrics, including pixel classification accuracy, Mean Intersection over Union (mIoU), Dice Coefficient, inference time per image, and Graphics Processing Unit (GPU) resource utilization. Results indicate that the optimized U-Net achieved the optimal balance between performance and efficiency, attaining a validation accuracy of 88.85%, mIoU of 76.72%, and a Dice score of 86.71%. With an inference time of 49.5 milliseconds per image, coupled with minimal GPU utilization (18%), the model proves suitable for real-time deployment in production environments. Conversely, complex architectures, such as DeepLabV3+, did not yield the anticipated benefits, thereby underscoring the viability of utilizing compact models for highly specialized industrial tasks. This study establishes a novel quantitative metric for the assessment of fermentation. This is based on the characteristics of the foam surface and thus offers an objective alternative to traditional subjective inspections. The findings emphasize the potential of adapting optimized deep learning architectures to quality control tasks within the food industry, particularly in the brewing sector, and they pave the way for further integration into automated computer vision systems. Full article

Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R. Full article

Natural disasters occur worldwide and cause significant financial and human losses. Wildfires are among the most important natural disasters, occurring more frequently in recent years due to global warming. Fast and accurate post-disaster damage detection could play an essential role in swift rescue planning and operations. Remote sensing (RS) data is an important source for tracking damage detection. Deep learning (DL) methods, as efficient tools, can extract valuable information from RS data to generate an accurate damage map for future operations. The present study proposes an encoder–decoder architecture composed of pre-trained Yolov11 blocks as the encoder path and Modified UNet (MUNet) blocks as the decoder path. The proposed network includes three main steps: (1) pre-processing, (2) network training, (3) prediction multilabel damage map and accuracy evaluation. To evaluate the network’s performance, the US and Canada datasets were considered. The datasets are satellite images of the 2023 wildfires in the US and Canada. The proposed method reaches the Overall Accuracy (OA) of 97.36, 97.47, and Kappa Coefficient (KC) of 0.96, 0.87 for the US and Canada 2023 wildfire datasets, respectively. Regarding the high OA and KC, an accurate final burnt map can be generated to assist in rescue and recovery efforts after the wildfire. The proposed YOLOv11–MUNet framework introduces an efficient and accurate post-event-only approach for wildfire damage detection. By overcoming the dependency on pre-event imagery and reducing model complexity, this method enhances the applicability of DL in rapid post-disaster assessment and management. Full article

Objective: To develop and evaluate artificial intelligence (AI)-based models for detecting laryngeal cancer using laryngoscope images. Methods: Two deep learning models were designed. The first identified and selected vocal cord images from laryngoscope datasets; the second localized laryngeal cancer within the selected images. Both employed FCN–ResNet101. Datasets were annotated by otolaryngologists, preprocessed (cropping, normalization), and augmented (horizontal/vertical flip, grid distortion, color jitter). Performance was assessed using Intersection over Union (IoU), Dice score, accuracy, precision, recall, F1 score, and per-image inference time. Results: The vocal cord selection model achieved a mean IoU of 0.6534 and mean Dice score of 0.7692, with image-level accuracy of 0.9972. The laryngeal cancer model achieved a mean IoU of 0.6469 and mean Dice score of 0.7515, with accuracy of 0.9860. Real-time inference was observed (0.0244–0.0284 s/image). Conclusions: By integrating a vocal cord selection model with a lesion detection model, the proposed platform enables accurate and fast detection of laryngeal cancer from laryngoscope images under the current experimental setting. Full article

Cerebrovascular diseases are a leading cause of global mortality, underscoring the need for objective and quantitative 3D visualization of cerebral vasculature from dynamic imaging modalities. Conventional analysis is often labor-intensive, subjective, and prone to errors due to image noise and subtraction artifacts. This study tackles the challenge of achieving fast and accurate volumetric reconstruction from angiography sequences. We propose a multi-stage pipeline that begins with image restoration to enhance input quality, followed by neural segmentation to extract vascular structures. Camera poses and sparse geometry are estimated through Structure-from-Motion, and these reconstructions are refined by leveraging the segmentation maps to isolate vessel-specific features. The resulting data are then used to initialize and optimize a 3D Gaussian Splatting model, enabling anatomically precise representation of cerebral vasculature. The integration of deep neural segmentation priors with explicit geometric initialization yields highly detailed 3D reconstructions of cerebral angiography. The resulting models leverage the computational efficiency of 3D Gaussian Splatting, achieving near-real-time rendering performance competitive with state-of-the-art reconstruction methods. The segmentation of brain vessels using nnU-Net and our trained model achieved an accuracy of 84.21%, highlighting the improvement in the performance of the proposed approach. Overall, our pipeline significantly improves both the efficiency and accuracy of volumetric cerebral vasculature reconstruction, providing a robust foundation for quantitative clinical analysis and enhanced guidance during endovascular procedures. Full article

Segmenting low-light images, such as images showing cracks on tunnel walls, is challenging due to limited visibility. Hence, we need to combine image brightness enhancement and a segmentation algorithm. We introduce essential preliminaries, specifically highlighting deep learning-based low-light image enhancement methods and the pixel-level image segmentation algorithm. After that, we provide a three-step low-light image segmentation algorithm. The proposed algorithm begins with brightness and contrast enhancement of low-light images, followed by accurate segmentation using a U-Net model. By various experimental results, we show the performance metrics of the proposed low-light image segmentation algorithm and compare the proposed algorithm’s performance against several baseline models. Furthermore, we demonstrate the implementation of the proposed low-light image segmentation pipeline on an edge computing platform. The implementation results show that the proposed algorithm is sufficiently fast for real-time processing. Full article

With the increasing prevalence of brain tumors, it becomes crucial to ensure fast and reliable segmentation in MRI scans. Medical professionals struggle with manual tumor segmentation due to its exhausting and time-consuming nature. Automated segmentation speeds up decision-making and diagnosis; however, achieving an optimal balance between accuracy and computational cost remains a significant challenge. In many cases, current methods trade speed for accuracy, or vice versa, consuming substantial computing power and making them difficult to use on devices with limited resources. To address this issue, we present PRA-UNet, a lightweight deep learning model optimized for fast and accurate 2D brain tumor segmentation. Using a single 2D input, the architecture processes four types of MRI scans (FLAIR, T1, T1c, and T2). The encoder uses inverted residual blocks and bottleneck residual blocks to capture features at different scales effectively. The Convolutional Block Attention Module (CBAM) and the Spatial Attention Module (SAM) improve the bridge and skip connections by refining feature maps and making it easier to detect and localize brain tumors. The decoder uses depthwise separable convolutions, which significantly reduce computational costs without degrading accuracy. The BraTS2020 dataset shows that PRA-UNet achieves a Dice score of 95.71%, an accuracy of 99.61%, and a processing speed of 60 ms per image, enabling real-time analysis. PRA-UNet outperforms other models in segmentation while requiring less computing power, suggesting it could be suitable for deployment on lightweight edge devices in clinical settings. Its speed and reliability enable radiologists to diagnose tumors quickly and accurately, enhancing practical medical applications. Full article

Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment, and ultrasound provides a fast, cost-efficient method for visualizing the median nerve. Reliable cross-sectional area (CSA) measurement remains challenging across imaging sites and varying scan qualities, and prior studies report segmentation Dice scores ranging from 0.76 to 0.93. Improving the robustness of automated segmentation is critical for achieving consistent, site-independent CSA assessment. This study evaluates a four-layer U-Net for automated segmentation and CSA estimation at two clinically relevant sites: the wrist crease and distal forearm. A primary dataset of 500 images per site was used to establish baseline performance. A second dataset of 35 wrist and 26 forearm still images was used to test generalizability, followed by intensity-based augmentations (CLAHE, gamma correction, speckle noise). Baseline models were tested on these new stills, and an augmented model was trained and evaluated on the combined datasets. The baseline models performed well on the first dataset but showed markedly reduced generalization on new still images (forearm IoU/Dice: 0.185/0.254; wrist IoU/Dice: 0.137/0.188). The augmented models improved within-set performance (forearm: 0.944/0.971; wrist: 0.951/0.974) and significantly enhanced generalization to new images (forearm: 0.408/0.533; wrist: 0.705/0.820). The final combined dataset models achieved Dice/IoU scores of 0.94/0.89 (forearm) and 0.96/0.92 (wrist). CSA measurements showed excellent to moderate correlation with manual tracings across all datasets. These findings demonstrate that targeted intensity-based augmentation substantially improves model generalization and enables robust, reproducible, and site-independent median nerve segmentation, supporting scalable ultrasound-based CTS assessment. Full article

In the field of infrared small target detection (ISTD), single-frame ISTD (SISTD), using only spatial features, cannot deal well with dim targets in cluttered backgrounds. In contrast, multi-frame ISTD (MISTD), utilizing spatio-temporal information from videos, can significantly enhance moving target features and effectively suppress background interference. However, current MISTD algorithms are limited by fixed-size time windows, resulting in an inability to adaptively adjust the input amount of spatio-temporal information for different detection scenarios. Moreover, utilizing spatio-temporal features remains a significant challenge in MISTD, particularly in scenarios involving slow-moving targets and fast-moving backgrounds. To address the above problems, we propose a memory-based temporal Transformer U-Net (MTTU-Net), which integrates a memory-based temporal Transformer module (MTTM) into U-Net. Specifically, MTTM utilizes the proposed D-ConvLSTM to sequentially transmit the temporal information in the form of memory, breaking through the limitation of the time window paradigm. And we propose a Transformer-based interactive fusion approach, which is dominated by spatial features of the to-be-detected frame and supplemented by temporal features in the memory, thereby effectively dealing with targets and backgrounds with various motion states. In addition, MTTM is divided into a temporal channel-cross Transformer module (TCTM) and a temporal space-cross Transformer module (TSTM), which achieve target feature enhancement and global background perception through feature interactive fusion in the channel and space dimensions, respectively. Extensive experiments on IRDST and IDSMT datasets demonstrate that our MTTU-Net outperforms existing MISTD algorithms, and they verify the effectiveness of the proposed modules. Full article

The aim of this research is to develop and implement artificial intelligence models for the automatic detection of defects in the microstructures of austempered ductile iron (ADI). Our research used three different approaches, representing various categories of machine learning tasks: image classification (ResNet), pixel-wise segmentation (UNet), and object detection (YOLO). Each of the models were adapted to the specific characteristics of the dataset and tested on a collection of microstructural images prepared within the scope of the research. The data preparation process included clustering using the k-means method, morphological operations, generation of binary masks, conversion of labels into formats required by each architecture, and data augmentation to increase the diversity of training samples. The results demonstrated that ResNet achieved very high classification accuracy but did not provide spatial information about defect localization. UNet produced precise segmentation masks of martensitic regions, allowing for quantitative analysis of samples, although it required significantly higher computational resources and struggled with detecting very small defects. YOLO, in turn, enabled fast detection of defects in the form of bounding boxes. In summary, each model proved effective in a different context: ResNet for preliminary classification, UNet for detailed laboratory analysis, and YOLO for industrial detection tasks. Full article

Background: Medical image segmentation is essential for accurate diagnosis and treatment planning. The U-Net architecture is widely regarded as the gold standard, yet its large size and high computational demand pose significant challenges for practical deployment. Methods: Real data (MRI images) from hospital patients were used in this study. We proposed a novel lightweight architecture tailored specifically for myocardium (cardiac muscle) segmentation. Results: We presented results comparable to state-of-the-art methods in terms of IoU and Dice coefficients. Nonetheless, the results achieved are much more favorable from the perspective of AI’s sustainable development. The proposed architecture ensured the following average results: IOU = 0.7889 and Dice = 0.8780 using 263 k parameters and a total of 6.24 G FLOPs. Conclusions: The proposed schema can potentially be used to support radiologists in improving the diagnostic process. The presented approach is efficient and fast. Most promisingly, the reduction in the model’s complexity is significant compared to the state-of-the-art methods. Full article