Search Results (333)

The complex aquatic environment attenuates light transmission, thereby limiting the detection range of underwater laser systems. To address the challenges of limited operational distance and significant light energy attenuation, this study investigates optimized underwater lighting and imaging applications using a combined tricolor RGB (RED-GREEN-BLUE) white laser source. First, accounting for the attenuation characteristics of water, we propose a power-compensated white laser system based on transmission distance and underwater imaging theory. Second, underwater experiments are conducted utilizing both standard D65 white lasers and the proposed power-compensated white lasers, respectively. Finally, the theory is validated by assessing image quality metrics of the captured underwater imagery. The results demonstrate that a low-power (0.518 W) power-compensated white laser achieves a transmission distance of 5 m, meeting the requirements for a long-range, low-power imaging light source. Its capability for independent adjustment of the three-color power output fulfills the lighting demands for specific long-distance transmission scenarios. These findings confirm the advantages of power-compensated white lasers in long-range underwater detection and refine the characterization of white light for underwater illumination. Full article

To date, visual analysis is mainly used to evaluate images of dehydrated films (facies) of biological fluids—microscopy at various magnifications, illumination with white or polarized light, as well as using a dark field. At the same time, important information on the architectonics of optically anisotropic supramolecular networks of facies is unknown (inaccessible). In our work, a model of optical anisotropy of the architectonics of supramolecular networks of blood facies is proposed. Algorithms and a methodology for a new multifunctional method of polarization-interference visualization of the Jones matrix and digital layer-by-layer phase reconstruction of optical anisotropy maps (theziograms) have been developed. As a result, statistically significant markers of oncological changes in the polycrystalline architectonics of supramolecular networks of blood facies samples from healthy donors and patients with papillary thyroid cancer at different stages of the oncological process have been determined and physically analyzed. A comparative study of the diagnostic efficiency of Jones matrix theziography (JT) and Mueller matrix diffusion tomography (MDT) of blood facies samples was conducted within the framework of evidence-based medicine. The main advantages of the Jones matrix method are shown: its multifunctionality (complex detection of birefringence and dichroism), high accuracy of early (stage 1: JM—90.4% and MDT—78.8%) and current (stage 2: JM—96.2% and MDT—88.5%) cancer diagnostics and an excellent level (JM—94.2% and MDT—84.6%) of differentiation of papillary thyroid cancer stages. Full article

Objectives: Anterior petrosectomy is a challenging neurosurgical procedure requiring precise identification and preservation of multiple critical structures. This technical note explores the feasibility of using real-time near-infrared indocyanine green (NIR-ICG) fluorescence with white light overlay to enhance visualization of the petrous internal carotid artery (ICA) during transpetrosal drilling. We aimed to assess its utility for planning and performing modified Dolenc–Kawase drilling. Methods: We integrated NIR-ICG and white light overlay using a robotic microscope with simultaneous visualization capabilities. This technique was applied to improve neurovascular preservation and skull base landmark identification. Intraoperative video frames and images were captured during an anterior transpetrosal approach for a petroclival meningioma, with technical details, surgical time, and feedback documented. Results: Real-time NIR-ICG with white light overlay successfully identified the posterior genu, horizontal petrosal segment, anterior genu, and superior petrosal sinus. It facilitated precise localization of cochlear landmarks, enabling tailored drilling of the Dolenc–Kawase rhomboid according to patient anatomy and accommodating potential anatomical variants. Conclusions: This approach could enhance intraoperative safety and improve exposure, possibly reducing neurovascular risks without extending operative time. It may serve as a valuable adjunct for complex skull base surgeries. Full article

Background: Esophageal strictures lacking typical malignant endoscopic features present a significant diagnostic challenge, often mimicking malignancy on imaging while concealing their true nature under regular white-light endoscopy. This study evaluated the utility of EUS-FNA in the differential diagnosis of such indeterminate strictures. Methods: We retrospectively analyzed 38 patients with suspicious malignant esophageal strictures indicated by CT but lacking definite malignant features on initial white-light gastroscopy. All patients underwent EUS-FNA for definitive pathological diagnosis. Clinicopathological data, imaging reports, endoscopic mucosal features, and procedural outcomes were assessed. Results: Among all 38 patients suspected of esophageal cancer by CT scan, 30 of them had malignant cytology results, including ESCC, EAC, metastatic cancer, and esophageal lymphoma. A total of 8 patients had benign findings, including esophageal tuberculosis, fungal esophagitis, eosinophilic esophagitis, and esophageal varices. Critically, EUS-FNA identified benign entities, such as eosinophilic esophagitis and esophageal tuberculosis masquerading as malignancy. CT features and mucosal features are also summarized and analyzed. Conclusions: EUS-FNA is a powerful tool for diagnosing esophageal strictures lacking typical malignant features. It reliably differentiates malignancy from challenging benign mimics, preventing misdiagnosis and guiding appropriate therapy. Clinicians should maintain a high suspicion for both occult malignancy and rare benign conditions in such stenotic lesions. Full article

Autopsy studies have shown that Alzheimer’s disease (AD) often coexists with cerebrovascular injury, affecting cognitive outcomes and the effectiveness of anti-amyloid-beta (Aβ) drugs. No fluid biomarkers of cerebrovascular injury have been identified yet. We investigated the association between white matter hyperintensities (WMH) severity and fluid biomarkers, including cerebrospinal fluid (CSF) neurofilament light chain and plasma placental growth factor (PlGF) levels. This study included 242 patients from memory clinics. Magnetic resonance imaging (MRI), CSF, and plasma samples were collected. Patients were classified as AD+ or non-AD based on the CSF Aβ42/Aβ40 ratio. In the discovery cohort (79 AD+ and 20 non-AD patients with 3D-T1 images), we analyzed the association between WMH volume and plasma PlGF. In the validation cohort (54 AD+ patients without 3D-T1 images), we analyzed the association between WMH grading and plasma PlGF. Among AD+ patients in the discovery cohort, plasma PlGF levels remained significantly associated with WMH volume and grading after adjusting for age, sex, and global cognition. Among the AD+ patients in the validation cohort, the high-PlGF (above median) group had significantly greater WMH volumes and a higher number of patients with a high WMH grading than the low-PlGF (below median) group. Plasma PlGF is a promising marker of cerebrovascular injury in AD. Full article

The rational design of artificial lighting systems in pig housing can enhance animal welfare, thereby boosting gilt health and reproductive performance while improving economic metrics for swine farms. To identify the optimal light environments for gilts under artificial illumination, we conducted self-selection-based photic preference testing, ultimately providing actionable insights for welfare-centric precision lighting protocols in modern pig production. In this study, a dynamic multi-chromatic self-selection system was developed, integrating programmable RGBW-LED arrays for spectral control, inter-compartment access channels for autonomous gilt movement, and real-time image recognition technology to investigate light color preferences. Twenty-four gilts (nulliparous female pigs) were housed for five weeks in pens with white, yellow, green, blue, or red light (100 lux), and they were given free access to all of the chromatic zones through inter-compartment channels. A YOLOv8n-based deep learning framework was used to quantify their spatiotemporal distribution, activity levels, and eating behavior. The key findings were the following: (1) a significant preference for green light environments (21.29 ± 3.77% distribution proportion) (p < 0.05), peaking at 6:00–13:00 and 18:00–20:00; (2) the average activity was the highest in a white light environment (25.49 ± 0.77%), significantly exceeding yellow (22.69 ± 0.63%) and green light (21.55 ± 0.61%) (p < 0.05); and (3) the daily feed consumption under green light was the lowest, significantly lower than that under white, blue, and red light (p < 0.05). The findings from this study offer insights into the light environment preferences of gilts, which could improve animal welfare. Full article

Background and Objectives: Early gastric cancer (EGC) has an excellent prognosis when detected, yet miss rates during endoscopy remain high. Narrow-band imaging (NBI) enhances mucosal and vascular visualization and is increasingly used, but its benefit over white-light imaging (WLI) in high-risk patients is uncertain. This study aimed to compare NBI with WLI for the detection of gastric neoplasia in patients undergoing gastroscopy. Materials and Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), registered in PROSPERO (CRD42025649908) and reported according to PRISMA 2020 guidelines. PubMed, Scopus, and CENTRAL were searched up to October 2024. Eligible RCTs randomized adults undergoing gastroscopy for cancer surveillance or red-flag symptoms to NBI or WLI. Data extraction and risk of bias assessment were performed independently by two reviewers. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and certainty of evidence was graded with GRADE. Results: From 21 records, 3 RCTs comprising 6003 patients were included. NBI did not significantly increase gastric neoplasm detection compared with WLI (2.79% vs. 2.74%; RR = 0.98; 95% CI: 0.66–1.45; I² = 22%). Focal gastric lesion detection rates (14.73% vs. 15.50%; RR = 1.05; 95% CI: 0.72–1.52; I² = 87%) and positive predictive value (29.56% vs. 20.56%; RR = 1.29; 95% CI: 0.84–1.99; I² = 61%) also showed no significant differences. Risk of bias was high for blinding, and overall evidence certainty was low. In practical terms, both NBI and WLI detected gastric cancers at similar rates, indicating that while NBI enhances visualization, it does not increase the likelihood of finding additional cancers in high-risk patients. Conclusions: NBI did not significantly improve gastric neoplasm detection compared with WLI in high-risk patients, though it remains valuable for mucosal and vascular assessment. Larger, multicenter RCTs across diverse populations are required to establish its role in surveillance strategies. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

Artificial intelligence (AI) is increasingly investigated as a potential adjunct in the diagnosis and staging of lung cancer, particularly through integration with bronchoscopy and endobronchial ultrasound (EBUS). Deep learning models have been applied to modalities such as white-light imaging, autofluorescence bronchoscopy, and spectroscopy, with the aim of assisting lesion detection, standardizing interpretation, and reducing interobserver variability. AI has also been explored in EBUS for lymph node assessment and guidance of transbronchial needle aspiration (EBUS-TBNA), with preliminary studies suggesting possible improvements in diagnostic yield. However, current evidence remains largely confined to small, retrospective, single-center datasets, often reporting performance under idealized conditions. External validation is rare, reproducibility is undermined by a lack of data and code availability, and workflow integration into real-world bronchoscopy practice has not been demonstrated. As such, most systems should still be regarded as experimental. Translating AI into routine thoracic oncology will require large-scale, prospective, multicenter validation studies, greater data transparency, and careful evaluation of cost-effectiveness, regulatory approval, and clinical utility. Full article

Lanthanide-doped inorganic luminescent materials have been extensively studied and applied in X-ray detection and imaging, anti-counterfeiting, and optical information storage. However, many reported rare-earth-based luminescent materials show only single-mode optical responses, which limits their applications in complex scenarios. Here, we report a novel Na₃KMg₇(PO₄)₆:Eu phosphor synthesized by a simple high-temperature solid-state method. The multi-color luminescence of Eu²⁺ and Eu³⁺ ions in a single matrix of Na₃KMg₇(PO₄)₆:Eu, known as radio-photoluminescence, is achieved through X-ray-induced ion reduction. It demonstrated a good linear response (R² = 0.9897) and stable signal storage (storage days > 50 days) over a wide range of X-ray doses (maximum dose > 200 Gy). In addition, after X-ray irradiation, this material exhibits photochromic properties ranging from white to brown in a bright field and shows remarkable bleaching and recovery capabilities under 254 nm ultraviolet light or thermal stimulation. This dual-modal luminescent phosphor Na₃KMg₇(PO₄)₆:Eu, which combines photochromism and radio-photoluminescence, presents a dual-mode X-ray detection and imaging strategy and offers a comprehensive and novel solution for applications in anti-counterfeiting and optical information encryption. Full article

External lighting is essential for autonomous inspections of concrete structures in low-light environments. However, previous studies have primarily relied on uniformly diffused lighting to illuminate images and faced challenges in detecting complex crack patterns. This paper proposes two novel algorithms that use directional lighting to classify concrete defects. The first method, named fused neural network, uses the maximum intensity pixel-level image fusion technique and selects the maximum intensity pixel values from all directional images for each pixel to generate a fused image. The second proposed method, named multi-channel neural network, generates a five-channel image, with each channel representing the grayscale version of images captured in the Right (R), Down (D), Left (L), Up (U), and Diffused (A) directions, respectively. The proposed multi-channel neural network model achieved the best performance, with accuracy, precision, recall, and F1 score of 96.6%, 96.3%, 97%, and 96.6%, respectively. It also outperformed the FusedNet and other models found in the literature, with no significant change in evaluation time. The results from this work have the potential to improve concrete crack classification in environments where external illumination is required. Future research focuses on extending the concepts of multi-channel and image fusion to white-box techniques. Full article

Lighting influences broiler production, health, and behavior. The objective of this study was to examine the effects of three light wavelengths (White [350–780 nm], Blue [450 nm], and Green [560 nm]) on broiler production, activity, fear, stress, blood cell profiles, and tibiotarsi (tibia) morphology. Day-of-hatch male broiler chicks (N = 600) were housed in pens (N = 12) with one lighting treatment for 42 days. Body weight and feed consumption were recorded on day (D) 0, 14, 28, and 42, and the feed conversion ratio was calculated. The Tonic Immobility test was used to assess the latency (seconds) to right from the testing cradle (D12 and D33). Blood was drawn for leukocyte and plasma corticosterone concentrations (D21 and D41). Accelerometers were attached at 2 and 5 weeks of age to measure activity. On D41, thermal images of the head were taken to measure surface temperatures (eye and beak), the bursa of Fabricius (bursa) was extracted for relative bursa weight, and the right and left tibias were extracted for tibia morphology. After sampling, the remaining broilers were processed. Activity was greatest in Green light at Week 2 (261.17 ± 8.52 m/s², p < 0.0001), and activity was lowest in White light at Week 5 (98.99 ± 8.52 m/s², p < 0.0001). Broilers under Blue light had lower concentrations of lymphocytes (8.62 ± 0.40 × 10³ cells/µL, p = 0.01) and T cells (7.16 ± 0.33 × 10³ cells/µL, p = 0.008) compared to White light. Breast yields were greatest (26.89 ± 0.13%, p = 0.01) in the White treatments compared to Green and Blue treatments. These results suggest that blue light may negatively impact immune health, while green light increases activity, without decreasing production performance, and white light can improve carcass traits. Therefore, lighting color can be strategically used to target welfare or production goals. Full article

Accurate and robust calibration of multifocal plenoptic cameras is essential for high-precision 3D light field reconstruction. In this work, we propose a blur feature-guided cascaded calibration for the plenoptic camera. First, white images at different aperture values are used to estimate the high-confidence center point and radius of micro-images, and the defocus theory is used to estimate the initial values of the intrinsic parameters. Second, the gradient value is introduced to quantify the degree of blurring of the corner points, which are then divided into three types: clear, semi-clear, and blurred. Furthermore, a joint geometric constraint model of epipolar lines and virtual depth is constructed, and the coordinates of the semi-clear and blurred corner points are optimized in a step-by-step manner by using the clear corner point coordinates. The micro-image center ray projection equation is then devised to assist in the optimization of the microlens array core parameters and establish blur-adaptive credibility weights, thereby constructing a global nonlinear optimization. Finally, the proposed method is tested on both simulated and captured datasets, and the results exhibit superior performance when compared with the established methods described by Labussière, Nousias, and Liu. The proposed method excels in corner feature extraction, calibration accuracy of both internal and external parameters, and calibration sensitivity when applied to multifocal-length light field cameras, highlighting its advantages and robustness. Full article

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient contrast often limits its effectiveness, making it challenging to distinguish between healthy and unhealthy tissues, particularly when identifying subtle mucosal and vascular abnormalities. These limitations have prompted the need for more advanced imaging techniques that enhance pathological visualization and facilitate early diagnosis. Therefore, this study investigates the integration of the Spectrum-Aided Vision Enhancer (SAVE) mechanism to improve WLI images and increase disease detection accuracy. This approach transforms standard WLI images into hyperspectral imaging (HSI) representations, creating narrow-band imaging (NBI-like) visuals with enhanced contrast and tissue differentiation, thereby improving the visualization of vascular and mucosal structures critical for diagnosing GI disorders. This transformation allows for a clearer representation of blood vessels and membrane formations, which is essential for determining the presence of GI diseases. The dataset for this study comprises WLI images alongside SAVE-enhanced images, including four categories: ulcerative colitis, polyps, esophagitis, and healthy GI tissue. These images are organized into training, validation, and test sets to develop a deep learning-based classification model. Utilizing principal component analysis (PCA) and multiple regression analysis for spectral standardization ensures that the improved images retain spectral characteristics that are vital for clinical applications. By merging deep learning techniques with advanced imaging enhancements, this study aims to create an artificial intelligence (AI)–driven diagnostic system capable of early and accurate detection of GI diseases. InceptionV3 attained an overall accuracy of 94% in both scenarios; SAVE produced a modest enhancement in the ulcerative colitis F1-score from 92% to 93%, while the F1-scores for other classes exceeded 96%. SAVE resulted in a 10% increase in YOLOv8x accuracy, reaching 89%, with ulcerative colitis F1 improving to 82% and polyp F1 rising to 76%. VGG16 enhanced accuracy from 85% to 91%, and the F1-score for polyps improved from 68% to 81%. These findings confirm that SAVE enhancement consistently improves disease classification across diverse architectures, offers a practical, hardware-independent approach to hyperspectral-quality images, and enhances the accuracy of gastrointestinal screening. Furthermore, this research seeks to provide a practical and effective solution for clinical applications, improving diagnostic accuracy and facilitating superior patient care. Full article

Background and Objectives: Multiple sclerosis (MS) is a chronic autoimmune, inflammatory, and neurodegenerative central nervous system disease. Neurodegeneration plays a central role in long-term disease progression. Materials and Methods: This cross-sectional study examined the relationship between neurodegenerative biomarkers, namely plasma neurofilament light chain (pNfL) levels and MRI-derived brain volume measurements, and clinical outcomes in 49 patients with relapsing–remitting multiple sclerosis (RRMS). Plasma NfL levels were quantified using Simoa technology, while MRI data was analyzed via FreeSurfer to measure volumes of grey and white matter, specific brain structures, and ventricular sizes. Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT) and Brief Visuospatial Memory Test-Revised (BVMT-R). Disability was evaluated using the Expanded Disability Status Scale (EDSS). Results: The results indicated significant positive correlations between SDMT scores and volumes of grey matter, white matter, and various subcortical structures, suggesting that preserved brain volume is linked to better cognitive performance. Negative correlations were observed between SDMT scores and ventricular volumes, as well as between SDMT scores and EDSS scores, implying that cognitive decline corresponds with structural brain deterioration and increased disability. No significant associations were found between BVMT-R scores and imaging data or disability measures. Plasma NfL levels showed significant correlations with early disease relapses and enlargement of the third and fourth ventricles, but not with brain volume, cognitive tests, or EDSS scores. Conclusions: These findings indicate that MRI-based brain volumetrics, particularly grey and white matter measures, are stronger indicators of cognitive function and disability in RRMS than plasma NfL. Full article