Search Results (228)

The aim of this study was to evaluate the performance and safety of T2769 (Thealoz^® Total), a preservative-free eye drop combining 0.15% sodium hyaluronate, 3% trehalose, and 2.45% N-acetylaspartyl-glutamate (NAAGA), in contact lens wearers with dry eye symptoms and discomfort. This prospective, single-arm investigation enrolled 34 adult contact lens wearers with Ocular Surface Disease Index (OSDI) scores ≥ 18 and Contact Lens Dry Eye Questionnaire-8 (CLDEQ-8) scores ≥ 12. Patients instilled one drop of T2769 three to six times daily for 36 days. Performance assessments included CLDEQ-8, ocular discomfort and symptoms, OSDI, soothing sensation, and ocular signs. Safety assessments included adverse events (AEs), far BCVA, and ocular tolerance. CLDEQ-8 improved from the baseline at Day 36 (−12.6 ± 5.0; p < 0.001) and as early as D15, with similar improvements in ocular discomfort, OSDI, and total symptom score. Soothing sensation was judged important by 79.4% of patients at D36. Ocular surface staining, tear break-up time, and the Schirmer test improved at D15 and D36, while conjunctival hyperaemia improved in 82.4% of patients at D36. Two non-serious treatment-related AEs (photophobia and blurred vision) occurred in one patient. BCVA was unchanged, and tolerance was rated very satisfactory/satisfactory. In conclusion, T2769 was safe and effective for reducing contact lens-associated dry eyes and discomfort. Full article

Landslide detection in mountainous regions remains highly challenging due to complex terrain conditions, heterogeneous surface textures, and the fragmented distribution of landslide features. To address these limitations, this study proposes an enhanced object detection framework named YOLOv11-SAFM, which integrates a Spatially Adaptive Feature Modulation (SAFM) module, an optimized MPDIoU-based bounding box regression loss, and a multi-scale training strategy. These improvements strengthen the model’s ability to detect small-scale landslides with blurred edges under complex geomorphic conditions. A high-resolution remote sensing dataset was constructed using imagery from Bijie and Zhaotong in southwest China including GF-2 optical imagery at 1 m resolution and Sentinel-2 data at 10 m resolution for model training and validation, while independent data from Zhenxiong County were used to assess generalization capability. Experimental results demonstrate that YOLOv11-SAFM achieves a precision of 95.05%, recall of 90.10%, F1-score of 92.51%, and mAP@0.5 of 95.30% on the independent test set of the Zhaotong–Bijie dataset for detecting small-scale landslides in rugged plateau environments. Compared with the widely used Mask R-CNN, the proposed model improves precision by 13.87% and mAP@0.5 by 15.7%; against the traditional YOLOv8, it increases recall by 27.0% and F1-score by 22.47%. YOLOv11-SAFM enables efficient and robust automatic landslide detection in complex mountainous terrains and shows strong potential for integration into operational geohazard monitoring and early warning systems. Full article

Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to estimate both the direction (in degrees) and weighted quantity (in pixels) of movement artifacts. Motivated by computational challenges in medical image quality assessment systems such as LUIAS, this work investigates directional multiplicative noise characterization using controlled experimental conditions with optical camera imaging. The MADE algorithm operates on multi-directional quantification outputs from a preprocessing pipeline—MAPE, ROPE, and MAQ. The methodology is designed for computational efficiency and instantaneous processing, providing interpretable outputs. Experimental results using precision-controlled apparatus demonstrate robust estimation of movement artifact direction and magnitude across a range of image shapes and velocities, with principal outputs aligning closely to ground truth parameters. The proposed MADE algorithm offers a methodological proof of concept for movement artifact analysis in single-frame images, emphasizing both directional accuracy and quantitative assessment under controlled imaging conditions. Full article

The Internet has been progressively more integrated into daily life through its evolutionary stages, ranging from web1.0 to the current development of web3.0. These continued integrations broaden the attack surface that cybercriminals aim to exploit. The prevalence of cybercrimes, particularly malware attacks, has become increasingly sophisticated and made more accessible through dark web marketplaces. Including artificial intelligence (AI) within anti-virus solutions has challenged the traditional dichotomy of malware detection schemes, offering more accurate and holistic detection capabilities. Research has shown that transforming malware files into textured images offers resistance to obfuscation and the potential to detect zero days. This paper explores the application of image quality assessment (IQA) techniques in enhancing visual malware dataset curation. We propose a novel framework that applies a no-reference IQA algorithm to evaluate current datasets and offer guidance in future dataset curation. Using multiple popular datasets, our evaluation demonstrates that the proposed MalScore framework effectively differentiates dataset quality—for example, MalNet Tiny achieves the highest score of 95%, while the NARAD malicious-image subset scores 50%. Additionally, BRISQUE was the only IQA algorithm to exhibit a strong linear sensitivity to blur levels across datasets. These results highlight the practical utility of MalScore in assessing and ranking visual malware datasets and lay the groundwork for uniting IQA and visual malware detection in future research. Full article

For a robot to operate robustly in diverse real-world environments, reliable obstacle perception is essential, which fundamentally requires depth information of the surrounding scene. Monocular depth estimation provides a lightweight alternative to active sensors by predicting depth from a single RGB image. However, due to the absence of sufficient geometric and optical cues, it suffers from inherent depth ambiguity. To address this limitation, we propose R-Depth Net, a monocular absolute depth estimation network that utilizes distance-dependent defocus blur variations and optical flow as complementary depth signals. Furthermore, based on the depth maps generated by R-Depth Net, we design an algorithm for obstacle height estimation and traversability assessment. Experimental results in real-world environments show that the proposed method achieves an average RMSE of 0.30 m (15.7%) and MAE of 0.26 m (15.7%) for distance estimation within the 1.0–3.0 m range. For obstacle height estimation in the range of 0.10–0.20 m, the system achieves an average RMSE of 0.048 m (29.3%) and MAE of 0.040 m (26.4%). Finally, real-time deployment on a quadruped robot demonstrates that the estimated depth and height are sufficiently accurate to support on-board obstacle traversal decision-making. Full article

Background/Objectives: The treatment of ovarian cancer (OC), which is predominantly diagnosed in advanced stages, poses a significant challenge to modern gynecologic oncology practice. A significant proportion of patients exhibit chemoresistance, underscoring the need for novel therapeutic interventions. This challenge is further compounded by the immunogenic nature of this neoplasm, prompting the exploration of alternative therapies. A notable example is the use of trastuzumab-deruxtecan (T-DXd), an antibody-drug conjugate (ADC), that has demonstrated encouraging outcomes in preliminary studies and has the potential to become a new treatment option. This systematic review aims to prove that. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) structure was employed to systematically search the PubMed and Scopus databases from December 2024. Furthermore, authors employed materials from the FDA’s official website and registry of clinical trials that are currently recruiting participants for T-DXd’s studies. Eligible studies included randomized controlled trials and observational studies assessing T-DXd in patients with OC. Outcomes of interest were objective response rate (ORR), median overall survival, adverse effects, and progression-free survival. Data was synthesized narratively. Results: Following a thorough review of available literature, 30 scientific papers were selected for inclusion. A total of 598 patients participated in clinical trials. The most common adverse effects were blurred vision and nausea, generally manageable. The risk of bias was low in most studies. Conclusions: T-DXd shows promising efficacy. A comparison of T-DXd with the ADC currently approved for OC therapy reveals that both demonstrate similar median overall survival and ORRs. However, the drug has exhibited significant adverse effects in breast cancer trials and has been studied on a relatively small number of patients. Therefore, further clinical trials focusing on OC patients are necessary to better assess the safety and efficacy of T-DXd in this population. Full article

Background/Objectives: Colonoscopy, a common outpatient procedure requiring bowel preparation, can lead to dehydration and electrolyte disturbances. Sedation, while improving patient comfort, may exacerbate these effects and contribute to orthostatic hypotension (OH) and postural orthostatic tachycardia syndrome (POTS). This study aimed to determine the prevalence of OH and POTS following sedated colonoscopy and to identify associated risk factors. Methods: This prospective observational study included 76 adult patients (ASA I–III) who underwent colonoscopy with fentanyl–propofol sedation between August and November 2024. Blood pressure, heart rate, and orthostatic intolerance (OI) symptoms were assessed before and after mobilization. OH was defined as a systolic blood pressure decrease ≥20 mmHg or diastolic decrease ≥10 mmHg upon standing. POTS was defined as a heart rate increase ≥30 bpm or an absolute heart rate ≥ 120 bpm. Statistical analyses were performed using SPSS 29.0. Results: Post-procedural OH and/or POTS occurred in 18 patients (23.7%), and 14 patients (18.4%) reported OI symptoms such as dizziness, nausea, or blurred vision. Symptomatic patients were significantly younger than asymptomatic patients (42.7 ± 15.4 vs. 54 ± 13.9 years, p = 0.009), and symptoms were more frequent among females (p = 0.046). Preoperative diastolic blood pressure was significantly higher in patients who developed OH (p = 0.022), while other hemodynamic and demographic variables showed no significant associations. Conclusions: Orthostatic hypotension and postural tachycardia are relatively common after sedated colonoscopy. Younger age and female sex were identified as independent risk factors for OI symptoms, suggesting a possible role of autonomic variability. Routine post-procedure monitoring and assisted mobilization before discharge may improve patient safety and recovery outcomes. Full article

¹⁸F-Florbetapir PET imaging is widely used to assess amyloid-β (Aβ) burden in the brain, particularly in the context of Alzheimer’s disease (AD). Conventional assessments typically rely on selected individual slices, which may limit spatial accuracy and are prone to image blurring. In the present study, we introduce novel techniques to enhance the spatial resolution and clarity of Aβ signal visualization in individuals pretreated with ¹⁸F-florbetapir. PET scans were retrospectively obtained from the Imaging and Data Archive for twelve individuals, including six cognitively unimpaired subjects and six diagnosed with AD. Each dataset consisted of 346 raw images, comprising 90 axial, 128 coronal, and 128 sagittal slices. Images were reconstructed into a single 3D volume using the 3D Slicer platform. Crucially, we applied artificial intelligence or AI-driven signal enhancement techniques to suppress background noise and amplify Aβ signals. This AI-enhanced processing improved image clarity and enabled visualization of subtle and spatially organized signal patterns. To verify anatomical location, Aβ PET signals were registered with MRI. This integrated workflow allowed us to visualize Aβ signals across regions of interest, including the brain parenchyma, skull, and cervical tissues. Our analytical approaches revealed that Aβ signals are highly concentrated and confined within non-CNS fluid compartments, forming canal-like networks that extend from the brain parenchyma toward the skull base, particularly the occipital clivus, and connect to the cervical lymph nodes. Additional Aβ signals were observed along the internal carotid plexus. These findings suggest that, when reconstruction in 3D and enhanced with AI, ¹⁸F-florbetapir PET imaging may not only reflect Aβ plaque burden in the brain but also visualize soluble Aβ species concentrated within anatomical clearance pathways leading to the peripheral lymphatic system. This approach offers a new dimension to PET signal interpretation and highlights the potential of AI-enhanced 3D in advancing neuroimaging analysis. Full article

Titanium dioxide (TiO₂) is widely used in coatings, plastics, rubber, papermaking, and other industries. The microstructural characteristics of its inorganic shell largely determine the overall performance of the product, significantly affecting optical behavior, dispersibility, weather resistance, and stability. Currently, coating quality evaluation in industry still relies primarily on manual inspection, lacking objective, standardized, and reproducible quantitative methods. This study focuses on lab-prepared core–shell TiO₂ powders comprising a TiO₂ core and a thin inorganic shell enriched in alumina/silica. This study presents Titanium Dioxide U-Net (TD U-Net)—a deep learning approach for transmission electron microscopy (TEM) image segmentation and shell thickness evaluation of core–shell structured TiO₂ particles. TD U-Net employs an encoder–decoder architecture that effectively integrates multi-scale features, addressing challenges such as blurred boundaries and low contrast. We constructed a dataset of 1479 TEM images processed through a six-step workflow: image collection, data cleaning, annotation, mask generation, augmentation, and cropping. Results show that TD U-Net achieves a Dice coefficient of 0.967 for segmentation accuracy and controls shell-thickness measurement error within 5%, significantly outperforming existing image-processing models. An intelligent analysis system developed from this technology has been successfully applied to titanium dioxide product quality assessment, providing an efficient and reliable automated tool for coating-process optimization and quality control. Full article

A method for super-resolution reconstruction of sonograms based on Residual Dense Conditional Generative Adversarial Network (RDC-GAN) is proposed in this paper. It is well known that the resolution of medical ultrasound images is limited, and the single-frame image super-resolution algorithms based on a convolutional neural network are prone to losing texture details, extracting much fewer features, and then blurring the reconstructed images. Therefore, it is very important to reconstruct high-resolution medical images in terms of retaining textured details. A Generative Adversarial Network could learn the mapping relationship between low-resolution and high-resolution images. Based on GAN, a new network is designed, where the generation network is composed of dense residual modules. On the one hand, low-resolution (LR) images are input into the dense residual network, then the multi-level features of images are learned, and then are fused into the global residual features. On the other hand, conditional variables are introduced into a discriminator network to guide the process of super-resolution image reconstruction. The proposed method could realize four times magnification reconstruction of medical ultrasound images. Compared with classical algorithms including Bicubic, SRGAN, and SRCNN, experimental results show that the super-resolution effect of medical ultrasound images based on RDC-GAN could be effectively improved, both in objective numerical evaluation and subjective visual assessment. Moreover, the application of super-resolution reconstructed images to stage the diagnosis of cirrhosis is discussed and the accuracy rates prove the practicality in contrast to the original images. Full article

Background: Cardiac-respiratory dual gating in SPECT (DG-SPECT) is an emergent technique for alleviating motion blurring artifacts in myocardial perfusion imaging (MPI) due to both cardiac and respiratory motions. Moreover, the attenuation artifact may arise from the spatial mismatch between the sequential SPECT and CT attenuation scans due to the dual gating of SPECT data and non-gating CT images. Objectives: This study adapts a four-dimensional (4D) cardiac SPECT reconstruction with post-reconstruction respiratory motion correction (4D-RMC) for dual-gated SPECT. In theory, a respiratory motion-matched attenuation correction (MAC) method is expected to yield more accurate reconstruction results than the conventional motion-averaged attenuation correction (AAC) method. However, its potential benefit is not clear in the presence of practical imaging artifacts in DG-SPECT. In this study, we aim to quantitatively investigate these two attenuation methods for SPECT MPI: 4D-RMC (MAC) and 4D-RMC (AAC). Methods: DG-SPECT imaging (eight cardiac gates and eight respiratory gates) of the NCAT phantom was simulated using SIMIND Monte Carlo simulation, with a lesion (20% reduction in uptake) introduced at four different locations of the left ventricular wall: anterior, lateral, septal, and inferior. For each respiratory gate, a joint cardiac motion-compensated 4D reconstruction was used. Then, the respiratory motion was estimated for post-reconstruction respiratory motion-compensated smoothing for all respiratory gates. The attenuation map averaged over eight respiratory gates was used for each respiratory gate in 4D-RMC (AAC) and the matched attenuation map was used for each respiratory gate in 4D-RMC (MAC). The relative root mean squared error (RMSE), structural similarity index measurement (SSIM), and a Channelized Hotelling Observer (CHO) study were employed to quantitatively evaluate different reconstruction and attenuation correction strategies. Results: Our results show that the 4D-RMC (MAC) method improves the average relative RMSE by as high as 5.42% and the average SSIM value by as high as 1.28% compared to the 4D-RMC (AAC) method. Compared to traditional 4D reconstruction without RMC (“4D (MAC)”), these metrics were improved by as high as 11.23% and 27.96%, respectively. The 4D-RMC methods outperformed 4D (without RMC) on the CHO study with the largest improvement for the anterior lesion. However, the image intensity profiles, the CHO assessment, and reconstruction images are very similar between 4D-RMC (MAC) and 4D-RMC (AAC). Conclusions: Our results indicate that the improvement of 4D-RMC (MAC) over 4D-RMC (AAC) is marginal in terms of lesion detectability and visual quality, which may be attributed to the simple NCAT phantom simulation, but otherwise suggest that AAC may be sufficient for clinical use. However, further evaluation of the MAC technique using more physiologically realistic digital phantoms that incorporate diverse patient anatomies and irregular respiratory motion is warranted to determine its potential clinical advantages for specific patient populations undergoing dual-gated SPECT myocardial perfusion imaging. Full article

Low-light image enhancement in architectural scenes presents a considerable challenge for computer vision applications in construction engineering. Images captured in architectural settings during nighttime or under inadequate illumination often suffer from noise interference, low-light blurring, and obscured structural features. Although low-light image enhancement and deblurring are intrinsically linked when emphasizing architectural defects, conventional image restoration methods generally treat these tasks as separate entities. This paper introduces an efficient and robust Frequency-Space Recovery Network (FSRNet), specifically designed for low-light image enhancement in architectural contexts, tailored to the unique characteristics of such scenes. The encoder utilizes a Feature Refinement Feedforward Network (FRFN) to achieve precise enhancement of defect features while dynamically mitigating background redundancy. Coupled with a Frequency Response Module, it modifies the amplitude spectrum to amplify high-frequency components of defects and ensure balanced global illumination. The decoder utilizes InceptionDWConv2d modules to capture multi-directional and multi-scale features of cracks. When combined with a gating mechanism, it dynamically suppresses noise, restores the spatial continuity of defects, and eliminates blurring. This method also reduces computational costs in terms of parameters and MAC operations. To assess the effectiveness of the proposed approach in architectural contexts, this paper conducts a comprehensive study using low-light defect images from indoor concrete walls as a representative case. Experimental results indicate that FSRNet not only achieves state-of-the-art PSNR performance of 27.58 dB but also enhances the mAP of the downstream YOLOv8 detection model by 7.1%, while utilizing only 3.75 M parameters and 8.8 GMACs. These findings fully validate the superiority and practicality of the proposed method for low-light image enhancement tasks in architectural settings. Full article

This study introduces a comprehensive benchmarking framework for evaluating visual odometry (VO) methods, combining classical, learning-based, and hybrid approaches. We assess 52 configurations—spanning 19 keypoint detectors, 21 descriptors, and 4 matchers—across two widely used benchmark datasets: KITTI and EuRoC. Six key trajectory metrics, including Absolute Trajectory Error (ATE) and Final Displacement Error (FDE), provide a detailed performance comparison under various environmental conditions, such as motion blur, occlusions, and dynamic lighting. Our results highlight the critical role of feature matchers, with the LightGlue–SIFT combination consistently outperforming others across both datasets. Additionally, learning-based matchers can be integrated with classical pipelines, improving robustness without requiring end-to-end training. Hybrid configurations combining classical detectors with learned components offer a balanced trade-off between accuracy, robustness, and computational efficiency, making them suitable for real-world applications in autonomous systems and robotics. Full article

Edible coatings are widely used to modulate oil uptake and moisture in fried foods. In this study, we evaluated a microfluid-assisted flow-blurring spray against conventional application by dipping/spraying, focusing on the coating efficiency and preliminary implications for sustainable process. This study combines benchtop experiments with a near-nozzle numerical analysis where the gas–liquid interface and primary breakup are modeled using the Volume of Fluid (VOF) approach implemented in OpenFOAM, configured for a flow-blurring geometry to generate whey protein isolate (WPI) coatings. Viscosity, density, solid content, and contact angle were validated experimentally and used in the simulation setup. An image-based droplet pipeline quantified spray characteristics, yielding a volumetric median diameter D₅₀ = 83.69 µm and confirming process uniformity. Contact angles showed marked substrate dependence: hydrophilic surfaces, 68°–85°; hydrophobic surfaces, 95°–110°. For turkey sausages, sessile-drop contact angles were not determinable (N.D.) due to wicking/roughness; wettability was therefore assessed on smooth surrogates and via performance metrics. Fit-for-purpose simulation procedures are outlined. Microfluidic application (WPI-McF) lowered oil uptake versus uncoated controls. Together, robust modeling, targeted image analytics, and high-precision microfluidics enable rational tuning of coating microstructure and barrier performance, offering a scalable pathway to reduce lipid content and enhance fried food quality. Full article

Landslide deformation monitoring via InSAR is crucial for assessing the risk of hazards. Quick and accurate detection of active deformation zones is crucial for early warning and mitigation planning. While the application of deep learning has substantially improved the detection efficiency, several challenges still persist, such as poor multi-scale perception, blurred boundaries, and limited model generalization. This study proposes Hybrid-SegUFormer to address these limitations. The model integrates the SegFormer encoder’s efficient feature extraction with the U-Net decoder’s superior boundary restoration. It introduces a multi-scale fusion decoding mechanism to enhance context perception structurally and incorporates a self-distillation strategy to significantly improve generalization capability. Hybrid-SegUFormer achieves detection performance (98.79% accuracy, 80.05% F1-score) while demonstrating superior multi-scale adaptability (IoU degradation of only 6.99–8.83%) and strong cross-regional generalization capability. The synergistic integration of its core modules enables an optimal balance between precision and recall, making it particularly effective for complex landslide detection tasks. This study provides a new approach for intelligent interpretation of InSAR deformation in complex mountainous areas. Full article