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Clinical relevance: Central retinal artery occlusion (CRAO) is an ophthalmic emergency with a poor prognosis. Several initial CRAO presentation factors can influence the final visual outcome. Fluorescein angiography filling of retinal vessels in CRAO has not been studied in great detail. Background: The aim of this paper is to study the aberrant filling of the retinal vessels on fluorescein angiography in patients with CRAO and understand its clinical relevance. Methods: Cases of CRAO diagnosed between June 2017 and May 2022 and who had undergone a fundus fluorescein angiography were included. Comparisons were made between the cases with and without aberrant filling of the retinal vessel. Results: Twenty-two eyes of 22 patients (14 males and 8 females) with CRAO underwent fluorescein angiography. Ages of the patients ranged from 18 to 73 years, while time interval between development of acute onset vision problems and presentation to the retinal clinic ranged from a minimum of 1 day to a maximum of 30 days. Snellen visual acuity at presentation ranged from perception of light (PL +) to 6/6. On fluorescein angiography, aberrant flow of the fluorescein dye into the retinal vein was observed in four of the 22 (18%) eyes with CRAO. All these cases showed a patient's temporal cilioretinal artery. The cases with aberrant filling of the retinal vessels showed a better presenting visual acuity (6/6–6/60), lesser severity of inner retinal damage and a better final visual acuity (6/6–6/60) compared to CRAO cases without aberrant filling. Conclusion: Aberrant filling of the retinal vein can be seen on fluorescein angiography in eyes with CRAO and a patent temporal cilioretinal artery. Such eyes with aberrant retinal vessel filling have a better visual prognosis. [ABSTRACT FROM AUTHOR]

High‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Automatic gender classification is an important task in facial analysis. Recently, deep learning has achieved remarkable progress in extracting deep features for face image analysis, particularly in gender classification. Gender classification through facial analysis poses a difficult challenge in extracting reliable and relevant features from facial images and classifying them accurately, especially in uncontrolled environments. So, in this study, a novel deep convolutional neural network (DCNN) aided with convolutional long short-term memory (ConvLSTM) layer and squeeze-and-excitation (SE) block is designed to extract most distinguishable and relevant features from facial images. The ConvLSTM layer aims to establish spatial relationship among the extracted feature maps, while the SE block enhances the emphasis on critical features by directing more attention towards them. The extracted features are pre-processed and deployed to Kernel principle component analysis (KPCA) for dimensionality reduction. Finally, these reduced features are fed into a stacking classifier that utilizes several machine learning classifiers such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost, Extra Tree Classifier (ETC), Gaussian Naive Bayes (GNB), XGBoost classifier (XBC), Support Vector classifier (SVC), Bagging Classifier, NuSVC, Gradient boosting classifier (GBC) for the first stage,and SVC as a meta-classifier for final gender prediction with Stratified K-fold cross-validation in order to mitigate the issue of overfitting. The proposed method was evaluated on two publicly available datasets, label faces in the wild (LFW) and Adience, and achieved accuracies of 99.20% and 98.01%, respectively. These results indicate that our proposed approach outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The family of pro-inflammatory and pro-angiogenic chemokines including Interleukin-8 (IL-8, aka CXCL8) and its homologues (CXCL1,2,3,5,6, and 7) exhibit promiscuous binding and activation of several G-protein-coupled receptors (i.e., CXCR2, CXCR1, and the atypical chemokine receptor (ACKR1)). A high proportion of their biological activity is attributed to CXCR2 activation, thus many CXCR2 inhibitors are in clinical trials for several chronic diseases. However, CXCR2 inhibition is often only investigated acutely in these trials or in Cxcr2−/− mice grown in gnotobiotic conditions. Since humans do not live in germ-free environments, our first goal is to highlight novel retinal and systemic observations in Cxcr2−/− mice grown in non-gnotobiotic conditions that suggest potential harmful consequences of long-term CXCR2 deficiency or blockade. Beyond confirmation of circulating blood/immune cell-related phenotypes, we report novel findings in Cxcr2−/− mice including: (1) delayed dye transit to the retinal vasculature, (2) alterations in the density and distribution of retinal vessels, astrocytes and microglia, (3) decreased electroretinogram a- and b-wave amplitudes, (4) reduced visual acuity, and (5) increased polymorphonuclear cell accumulation in vascular lumina abutting venular walls in the retina and in vital non-ocular tissues (lung and liver). Furthermore, PheWAS of CXCR2 CXCR1, and ACKR1 gene variants using data from UK Biobank participants suggest clinical associations with both retinal and vascular disease phenotypes. We conclude that chronic CXCR2 deficiency in mice contributes to functional damage to the retina and that the long-term safety of CXCR1/2 inhibitors designed for chronic use in humans should be explored before clinical adoption to safeguard sight and overall vascular health. [ABSTRACT FROM AUTHOR]

The utilization of retinal vessel images is prevalent in the diagnosis of numerous diseases, such as chronic vascular diseases, diabetic retinopathy, and glaucoma. Despite the U-Net model's effective performance in retinal vessel imaging, it has been observed that the U-Net model fails to adequately detect dense blood vessels and vascular bifurcation. This research proposes a retinal vessel segmentation model that is both efficient and straightforward. The study introduces two significant contributions. Firstly, the multi-scale structure is improved, and a feature circulation module is added to enhance the detection of blood vessels. Secondly, an AGM module is incorporated into the jump connection to boost the transmission of context characteristics and suppress background noise. The presented model is assessed using publicly available DRIVE and STARE datasets. The proposed method and U-Net evaluation results are quantitatively and qualitatively analyzed, alongside a comparison with current state-of-the-art algorithms. By conducting a comparative analysis, certain algorithmic indices have exhibited state-of-the-art performance. The AttMSFCU-Net model, as introduced in this study, achieves advanced or comparable accuracy levels when evaluated on both the DRIVE and STARE datasets. Of particular note is the enhanced segmentation performance of dense and bifurcation vessels. The evaluation of the proposed model on the DRIVE and STARE datasets highlights its robustness and accuracy, affirming its utility in practical applications. [ABSTRACT FROM AUTHOR]

To extract maximum features ResAttNet (RAN) network structure is chosen as an alternative to the convolutional layer and it enhances image feature extraction. Additionally, a Deformable Convolution (DC) network was included to provide a feature extraction module, improving the model's capacity to simulate vessel deformation. Apart from the two additional networks because of inadequate quality in retinal data, before model building pre-processing is done. The data is processed by CLAHE, normalization, grayscale transformation, and gamma transformation. Second, the fundamental network structure model U-net is constructed, and the ResAttNet (RAN) structure and DC network are combined to form the ARDC-UNet network. Experimental data, both quantitative and qualitative, demonstrate the efficiency and accuracy with which our ARDC-UNet can segment retinal vessels. [ABSTRACT FROM AUTHOR]

This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffusion process, this approach significantly improves the effectiveness and efficiency of enhancing low-quality fundus images. The model achieves high-precision enhancement of retinal vessel details while substantially reducing the number of parameters. Comprehensive tests on publicly available datasets show that the suggested approach surpasses various advanced low-light image enhancement methods in both quantitative and qualitative assessments. [ABSTRACT FROM AUTHOR]

Retinal blood vessel segmentation is important for detection of several highly prevalent, vision-threatening diseases such as diabetic retinopathy. Automatic retinal blood vessel segmentation is crucial to overcome the limitations posed by diagnoses by doctors. In recent times, deep learning-based methods have achieved great success in automatically segmenting retinal blood vessels from images. In this paper, a U-Net-based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Three pre-processing algorithms are proposed to enhance the performance of the proposed method further. Based on experimental evaluation of the publicly available DRIVE dataset, the proposed method achieves 0.9577 average accuracies (Acc), 0.7436 sensitivity (Se), 0.9838 specificities (Sp) and 0.7931 F1-score. The proposed method outperforms the recent state-of-art approaches in the literature. [ABSTRACT FROM AUTHOR]

Background: Affections of eye are commonly encountered in all the species of animals. The age-related changes of the retina primarily cause loss of visual acuity as well as reduction of the visual field. Hence, the current study was carried out to establish basic data related to ageing changes in the retina of buffaloes. Methods: The study was conducted on 63 eye balls of locally available buffaloes of different breeds. These buffaloes were categorized into 3 groups i.e., group I (1-5 yrs), group II (6-10 yrs) and group III (11 yrs and above). The eyeballs were isolated and fixed with Davidson's fluid. The paraffin sections were subjected for routine histological study. Result: The thickness of retinal pigment epithelium was increased from group I to III buffaloes from 6.06±0.18 to 8.44±0.32 and the quantity of melanin pigment was decreased with advancement of age. Rod and cone cells of the photoreceptor layer were tightly packed in young age, loosely arranged in old animals. The mean thickness (µm) of photoreceptors and outer nuclear layers together was decreased from 64.22±1.84 to 58.72±2.1 with age advancement. The displacement of nuclei from outer nuclear layer into outer plexiform layer was significantly decreased in old animals. The outer limiting membrane was continuous and uninterrupted throughout the life. The thickness of outer plexiform layer was increased with advancement of age from 8.78±0.58 to 10±0.51 due to enhancement of synaptic fibers density. As age advances the number and density of horizontal, bipolar and amacrine cells were decreased and also the mean thickness (µm) of this layer was decreased from 30.83±1.48 to 22.56±0.62 in the inner nuclear layer. The thickness of inner plexiform layer was increased from 42.44±2.23 to 45.39±0.81 with advancement of age due to increased cystoids spaces and thickening of retinal blood vessels. In ganglionic cell layer, the number of β-ganglion cells were more than the β-ganglion cells, their common number were decreased approximately from 16-20 cells/sq.mm to 7-10 cells/sq.mm, whereas the average size was increased from 4.72±0.49 to 15.83±0.83 with advancement of age. In aged buffaloes nerve fiber layer showed corpora amylacea and thickened blood vessels. The inner limiting membrane became thick and uninterrupted in old buffaloes. The total thickness (µm) of retina in group I, II and III buffaloes were 235.5±7.25, 184.33±3.64 and 201.05±5.15 respectively. [ABSTRACT FROM AUTHOR]

Motivation Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. Results In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (⁠ L h f ⁠) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and L h f ⁠ , both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and L h f with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. Availability and implementation Code and data are openly accessible at https://github.com/cbmi-group/MRE. [ABSTRACT FROM AUTHOR]

Purpose: The clinical use of en face optical coherence tomography (OCT) has revealed nerve fiber layer clefts in the retinal nerve fibers in eyes with macula-centered epiretinal membranes (ERMs). The purpose of this study is to describe the location and the extent of retinal nerve fiber layer (RNFL) clefts in eyes with symptomatic ERMs. Methods: We conducted a retrospective review of 17 individual eyes in 17 patients with symptomatic ERMs and a control group of 10 healthy eyes from 10 subjects who had been examined for unrelated causes. The examinations performed included best-corrected visual acuity, rebound tonometry, fundus photography, structural OCT and angiographic OCT (OCTA) made in the form of 12 × 12 mm angiographic volume scans. Results: Hyporeflective RNFL clefts, seen in 14 out of 17 eyes with ERMs, were sharply demarcated in the en face presentation of slabs extending from the internal limiting membrane through the RNFL or including only the latter. The clefts were capillary-free on OCTA scans and formed depressions of the retinal surface. Most of the clefts were adjacent to and followed the course of the retinal trunk vessels, but clefts were also seen along smaller macular vessels and beyond the retinal vascular arcades. Conclusions: Paravascular RNFL clefts can be observed beyond the vascular arcades and adjacent to small vessels on OCTA block scan data. This suggests that the direction and magnitude of tractional displacement of the inner retina in eyes with epimacular membranes can extend beyond the vascular arcades and add to an improved analysis of abnormal fundus findings. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the impact of compensating retinal nerve fiber layer (RNFL) thickness for demographic and anatomical factors on glaucoma detection in Chinese and Indian adults. A population‐based study included 1995 healthy participants (1076 Chinese and 919 Indians) to construct a multivariable linear regression compensation model. This model was applied to 357 Chinese glaucoma patients, 357 healthy Chinese, and 357 healthy Indians using Cirrus spectral‐domain optical coherence tomography (OCT). The compensated RNFL thickness considered age, refractive error, optic disc parameters, and retinal vessel density. Results showed that although the average RNFL thickness was significantly higher in Chinese participants compared to Indians, the compensation model reduced this difference to nonsignificance. Moreover, the compensation model significantly improved the area under the receiver operating characteristic curve (0.90 vs. 0.78; p<0.001), sensitivity (75% vs. 51%), and specificity (67% vs. 32%) in distinguishing Chinese glaucoma patients from healthy Indian individuals. The compensation model significantly enhanced the diagnostic accuracy of RNFL thickness in distinguishing glaucoma in the Chinese ethnic group compared to the OCT instrument's default values. These results suggest that modifying RNFL measurements based on individual characteristics can yield substantial benefits for glaucoma detection across ethnicities. [ABSTRACT FROM AUTHOR]

Objectives: The effects of HIV and antiretroviral therapy (ART) on microvascular function are poorly explored. We compared retinal vessel functional responses to flicker light‐induced provocation (FLIP) in people living with HIV (PLWH) and people living without HIV (PLWoutH). Methods: We included 115 PLWH and 51 PLWoutH with a median age of 41 years. Treated PLWH received similar first‐line fixed‐dose combination ART. Clinical characteristics and retinal vessels functional responses to FLIP were compared in (a) PLWH and PLWoutH; and (b) PLWH groups stratified by the median of (i) CD4‐count (511 cells/mm3), (ii) viral load (50 copies/mL), and (iii) ART duration (57.6 months). Results: PLWH were older, smoked more, and had a lower prevalence of hypertension than PLWoutH (p < 0.05). Almost 64% of PLWH were infected for more than 5 years. Retinal vessel responses to FLIP were similar between PLWH and PLWoutH after taking confounders into account. In addition, PLWH subgroups stratified according to immuno‐virological status by CD4‐count, viral load, and ART duration showed no differences in retinal vessel responses to FLIP. Conclusion: Living with HIV and receiving ART were not associated with altered microvascular function as assessed with dynamic retinal vessel analysis in a South African case–control study. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence can predict the age of an individual using color fundus photographs (CFPs). This study aimed to investigate the accuracy of age prediction in the Kumejima study using fundus parameters and to clarify age-related changes in the fundus. Methods: We used nonmydriatic CFPs obtained from the Kumejima population study, including 1,646 right eyes of healthy participants with reliable fundus parameter measurements. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red–green–blue intensity in eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. Least absolute shrinkage and selection operator regression with leave-one-out cross-validation was used to predict age. The relationship between the actual and predicted ages was investigated using Pearson's correlation coefficient. Results: The mean age of included participants (834 males and 812 females) was 53.4 ± 10.1 years. The mean predicted age based on fundus parameters was 53.4 ± 8.9 years, with a mean absolute error of 3.64 years, and the correlation coefficient between actual and predicted age was 0.88 (p < 0.001). Older patients had greater red and green intensities and weaker blue intensities in the peripapillary area (p < 0.001). Conclusions: Age could be predicted using the CFP parameters, and there were notable age-related changes in the peripapillary color intensity. The age-related changes in the fundus may aid the understanding of the mechanism of fundus diseases such as age-related macular degeneration. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the roles of sphingosine kinases (SphKs) and sphingosine-1-phosphate receptors (S1PRs) in endotoxin-induced uveitis (EIU) mice. Methods: EIU model was induced using an intraperitoneal injection of lipopolysaccharide (LPS). The expression of SphKs and S1PRs in the retina was assessed using quantitative polymerase chain reaction (qPCR) and immunofluorescence. The effects of S1PR antagonists on the expression of inflammatory cytokines in the retina were evaluated using qPCR and western blotting. Effects of leukocyte infiltration of the retinal vessels were evaluated to determine the effects of the S1PR2 antagonist and genetic deletion of S1PR2 on retinal inflammation. Results: Retinal SphK1 expression was significantly upregulated in EIU. SphK1 was expressed in the GCL, IPL, and OPL and S1PR2 was expressed in the GCL, INL, and OPL. Positive cells in IPL and OPL of EIU retina were identified as endothelial cells. S1PR2 antagonist and genetic deletion of S1PR2 significantly suppressed the expression of IL-1α, IL-6, TNF-α, and ICAM-1, whereas S1PR1/3 antagonist did not. Use of S1PR2 antagonist and S1PR2 knockout in mice significantly ameliorated leukocyte adhesion induced by LPS. Conclusion: SphK1/S1P/S1PR2 signaling was upregulated in EIU and S1PR2 inhibition suppressed inflammatory response. Targeting this signaling pathway has potential for treating retinal inflammatory diseases. [ABSTRACT FROM AUTHOR]

Macrophage-like cells (MLC) have a fundamental role in the maintenance of immunosurveillance, response to inflammation and tissue injury in the retina. MLC can be visualized in vivo with conventional en face optical coherence tomography (OCT). The aim of this study is to describe this population of cells in active toxoplasmosis. We present two cases of active toxoplasma retinochoroiditis imaged at 2 time points, where the MLC were threshold after image processing and averaging for removing background and noise. In both patients the MLC collocated with the area of ischemia at the level of the choriocapillaris and retinal vessels. [ABSTRACT FROM AUTHOR]

The segmentation of retinal blood vessels in fundus images is critical for the diagnosis and analysis of various ocular, circulatory, and neurological conditions. Accurate segmentation aids in the early detection of diseases such as diabetic retinopathy and glaucoma. Traditional automated segmentation methods often rely on extensive labeled datasets for model pre-training, which limits their generalization capacity. Recent advancements in artificial intelligence and computer vision have introduced foundational models, such as the Segment Anything Model (SAM), which demonstrate strong zero-shot segmentation performance and transferability in natural image processing. However, SAM's application to medical imaging, particularly in retinal vessel segmentation, has produced suboptimal results. This study proposes an improved approach to retinal fundus image segmentation by integrating a Generative Adversarial Network (GAN)-based data augmentation technique to enhance training data diversity. Additionally, a dynamic batch size mechanism was introduced, optimizing the loss function for mask prediction and allowing flexible control over slice selection during loss calculation. This dual enhancement aims to improve the precision of blood vessel segmentation in retinal fundus images, overcoming the limitations observed in previous applications of SAM to medical image segmentation. The proposed method demonstrates potential for advancing the accuracy of retinal vessel segmentation, providing a robust tool for clinical diagnosis. [ABSTRACT FROM AUTHOR]

The segmentation of retinal vessels is vital for timely diagnosis. and treatment of various eye diseases. However, due to inherent characteristics of retinal vessels in fundus images such as changes in thickness, direction, and complexity of vessels, as well as imbalanced contrast between background and vessels, segmenting retinal vessels continues to pose significant challenges. Also, despite advancements in CNNbased methods, challenges such as insufficient extraction of structural information, complexity, overfitting, preference for local information, and poor performance in noisy conditions persist. To address these drawbacks, in this paper we proposed a novel modified U-Net named DABT-U-Net. Our method enhances discriminative capability by introducing Hierarchical Dilated Convolution (HDC), Dual Attentive BConvLSTM, and Multi-Head Self-Attention (MHSA) blocks. Additionally, we adopt a collaborative patch-based training approach to mitigate data scarcity and overfitting. Evaluation on the DRIVE and STARE datasets shows that DABT-U-Net achieves superior accuracy, sensitivity, and F1 score compared to existing methods, demonstrating its effectiveness in retinal vessel segmentation. Specifically, our proposed method demonstrates improvements in accuracy, sensitivity, and F1 score by 0.32%, 0.61%, and 0.14%, respectively, on the DRIVE dataset, and by 0.07%, 0.83%, and 0.14% on the STARE dataset compared to a less effective approach. [ABSTRACT FROM AUTHOR]

Background and Objective: This study aimed to report retinal displacement incidence following rhegmatogenous retinal detachment (RRD) repair with pars plana vitrectomy (PPV), scleral buckle (SB), or combined SB/PPV. Patients and Methods: A single-center, case series using Optos fundus autofluorescence (FAF) images was performed after RRD repair between April 2020 and February 2022. Retinal displacement was identified by imprinted retinal vessels on FAF imaging. Results: One hundred ninety-four eyes were included. Ninety-seven (50.0%) eyes underwent PPV, 46 (23.7%) underwent SB, and 51 (26.3%) underwent SB/PPV. A total of 25/194 eyes (12.9%) had retinal vessel imprinting on FAF imaging consistent with retinal displacement. The SB/PPV group (9/51, 17.6%) and the PPV group (15/97, 15.5%) had significantly higher displacement rates when compared to the SB group (1/46, 2.2%; P = 0.017, P = 0.021, respectively). Conclusions: Retinal displacement after RRD repair is more prevalent after PPV. There was a low rate of displacement with SB. Retinal displacement was not associated with a visual acuity difference. [Ophthalmic Surg Lasers Imaging Retina 2024;55:560–566.] [ABSTRACT FROM AUTHOR]

Aim: To investigate the associations between insulin use and diabetic retinopathy (DR), and retinal vascular parameters in type 2 diabetes (T2DM). Methods: A total of 6,374 T2DM patients, consisting of 2,231 patients receiving insulin alone and 4143 patients without any hypoglycemic medication, were included in cross-sectional analyses. Among those without DR at baseline, 791 patients were followed for three years in longitudinal analyses. Fundus photography was taken to diagnose DR and calculate central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), arteriolar-to-venular ratio (AVR), and vascular tortuosity. Inverse probability treatment-weighted analyses were performed. Results: After adjusting for gender, age, body mass index, blood pressure, blood glucose, T2DM duration, smoking, and alcohol use, insulin users showed a higher risk of DR (odds ratio (OR) = 2.27, 95% confidence interval (95%CI) = 2.08–2.48, P < 0.001), larger CRVE (β = 3.92, 95%CI = 2.46–5.37, P < 0.001), smaller AVR (β=-0.0083, 95%CI=-0.0121- -0.0046, P < 0.001), and larger vascular curvature (β = 0.19, 95%CI = 0.05–0.33, P = 0.008). After 3 years, insulin users had a higher risk of developing DR (OR = 1.94; 95% CI = 1.37–2.73, P = 0.002), and greater change in CRVE (β = 3.92, 95%CI = 0.96–6.88, P = 0.009). Conclusions: The impact of insulin on the retinal microvasculature provides support for linking insulin to the increased risk of DR, as well as cardiovascular events in T2DM. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the changes in retinal vascular structures associated with various stages of myopia by designing automated software based on an artificial intelligence model. Methods: The study involved 1324 pediatric participants from the National Children's Medical Center in China, and 2366 high-quality retinal images and corresponding refractive parameters were obtained and analyzed. Spherical equivalent refraction (SER) degree was calculated. We proposed a data analysis model based on a combination of the Convolutional Neural Networks (CNN) model and the attention module to classify images, segment vascular structures, and measure vascular parameters, such as main angle (MA), branching angle (BA), bifurcation edge angle (BEA) and bifurcation edge coefficient (BEC). One-way ANOVA compared parameter measurements between the normal fundus, low myopia, moderate myopia, and high myopia groups. Results: The mean age was 9.85 ± 2.60 years, with an average SER of -1.49 ± 3.16D in the right eye and − 1.48 ± 3.13D in the left eye. There were 279 (12.38%) images in the normal group and 384 (16.23%) images in the high myopia group. Compared with normal fundus, the MA of fundus vessels in different myopic refractive groups was significantly reduced (P = 0.006, P = 0.004, P = 0.019, respectively), and the performance of the venous system was particularly obvious (P < 0.001). At the same time, the BEC decreased disproportionately (P < 0.001). Further analysis of fundus vascular parameters at different degrees of myopia showed that there were also significant differences in BA and branching coefficient (BC). The arterial BA value of the fundus vessel in the high myopia group was lower than that of other groups (P = 0.032, 95% confidence interval [CI], 0.22–4.86), while the venous BA values increased (P = 0.026). The BEC values of high myopia were higher than those of low and moderate myopia groups. When the loss function of our data classification model converged to 0.09, the model accuracy reached 94.19%. Conclusion: The progression of myopia is associated with a series of quantitative retinal vascular parameters, particularly the vascular angles. As the degree of myopia increases, the diversity of vascular characteristics represented by these parameters also increases. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is termed as ever-lasting retinal disorders which can cause loss of vision and even blindness in most of the cases. The procedure to classify the level of severity in DR and is complex process as lesion features are complex to examine. The task of screening needs an effectual detection technique for classifying the subtle pathologies in retina. The deep models is considered as an imperative domain treating the disease caused in eye and help the ophthalmologists to make timely decisions. A new LeNet model is developed with Chronological Rat Swarm Optimization (CRSO) for detecting DR. The first step is the capturing retinal images through fundus photography, which further undergoes pre-processing with median filter. The segmentation is then achieved wherein lesion segmentation is completed with U-Net and blood vessel segmentation is performed with Dense U-Net. From input fundus image, lesion segmented output, and blood vessel segmented output, extraction of essential feature is performed. Finally, DR detection is executed with CRSO-based LeNet. The CRSO-based LeNet attained superior performance with highest accuracy of 89%, NPV of 87.2%, PPV of 87.0%, TNR of 89.0% and TPR of 89.5%. The proposed method is useful in various applications, such as medical applications and healthcare applications. [ABSTRACT FROM AUTHOR]

Background: Low-density lipoprotein cholesterol (LDL-C) is acknowledged as an independent risk factor (IRF) for atherosclerotic cardiovascular disease. Nevertheless, studies on the impact of LDL-C on microvasculature are still scarce. The retina, abundant in microvasculature, can now be examined for microvascular alterations through the novel, non-invasive, and quantitative optical coherence tomography angiography (OCTA) technique. Methods: In this cross-sectional study, 243 patients from the geriatric department were recruited (between December 2022 and December 2023). Individuals were classified into four groups based on their LDL-C levels: Group 1 (≤ 1.8 mmol/L), Group 2 (> 1.8 mmol/L to ≤ 2.6 mmol/L), Group 3 (> 2.6 mmol/L to ≤ 3.4 mmol/L), and Group 4 (> 3.4 mmol/L). The OCTA results including retinal vessel density (VD), foveal avascular zone (FAZ) area, macula thickness, and retinal nerve fiber layer (RNFL) thickness were contrasted across these groups. T-tests, analysis of variance, Welch's tests, or rank-sum tests were employed for statistical comparisons. In cases where significant differences between groups were found, post-hoc multiple comparisons or rank-sum tests were performed for pairwise group comparisons. Spearman's correlation coefficient was employed to perform bivariate correlation analysis to evaluate the relationship between LDL-C levels and various OCTA measurements. Multivariable regression analysis was used to evaluate the association between LDL-C levels and various OCTA measurements. Linear regression analysis or mixed-effects linear models were applied. Results: It was discovered that individuals with LDL-C levels exceeding 2.6 mmol/L (Groups 3 and 4) exhibited reduced VD in the retina, encompassing both the optic disc and macular regions, compared to those with LDL-C levels at or below 2.6 mmol/L (Groups 1 and 2). A negative correlation among LDL-C levels and retinal VD was identified, with r values spanning from − 0.228 to -0.385. Further regression analysis presented β values between − 0.954 and − 2.378. Additionally, no notable disparities were detected among the groups regarding FAZ area, macular thickness, and RNFL thickness. Conclusions: The outcomes of this study suggest that elevated LDL-C levels constitute an IRF for decreased VD across the entire retina. Trial registration: NCT05644548, December 1, 2022. [ABSTRACT FROM AUTHOR]

ABSTRACT Our study represents the first attempts to describe the ultrastructure features in addition to SEM‐EDX analysis of the pecten oculi of the nocturnal, visually active Eurasian stone‐curlew. The present study was carried out on 10 normal pecten oculi from the migrant Eurasian stone‐curlew (Burhinus oedicnemus) that was captured from the Northern Egyptian coast, Edku Lake, Edku, Behera, Egypt. The intraocular folded quadrilateral fan black pecten oculi was observed on the posteroinferior wall of the eyeball. The pecten oculi had 18–20 pleats, with narrow spaces in‐between. Our SEM findings showed a tortuous, coiled pecteneal surface due to numerous blood capillaries. The apical part was oriented toward the ciliary body, but the basal part was derived from the retina, just behind the optic nerve. The outer pleat surfaces are covered by the vitreopecteneal limiting membrane, which carries numerous hyalocyte cells on the outer pleat surface. The transverse sections revealed numerous blood capillaries surrounded by numerous melanin granules. Each basal part was surrounded by a thin, elevated ridge. Our SEM‐EDX analysis revealed that carbon is the highest (half percent), oxygen represents about one‐third, and nitrogen is one‐fifth percent of all elements. The lowest elements are sulfate at the apical part and phosphorus in the middle and basal parts. In conclusion, our findings reveal that the number of pecteneal pleats is related to the nocturnal active visual pattern and the lifestyle of this migratory bird. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes is a chronic autoimmune disease associated with insulin-producing beta cell destruction, declining insulin secretion, and elevated blood glucose. Physical activity improves glycaemic control and cardiovascular health. This study explores acute effects of maximal exhaustion induced by a cardiopulmonary exercise on macro- and microvascular parameters in type 1 diabetes. Methodology: Twenty-five participants with type 1 diabetes (14 males, 11 females), aged 41.4 ± 11.87 years, BMI 23.7 ± 3.08, completed a repeatedmeasure study. Measurements pre-, post-, 30- and 60-minutes post-exhaustion involved a maximal incremental cardio-pulmonary exercise test. Macro- and microvascular parameters were assessed using VICORDER® and retinal blood vessel image analysis. Repeated measures ANOVA in SPSS (Version 27.0) analysed data. Results: Post-exercise, heart rate increased (p<.001), and diastolic blood pressure decreased (p=.023). Diabetes duration correlated with pulse wave velocity (r=0.418, p=.047), diastolic blood pressure (r=0.470, p=.023), and central retinal arteriolar equivalent (r=0.492, p=.023). Conclusion: In type 1 diabetes, cardiopulmonary exercise-induced exhaustion elevates heart rate and reduces diastolic blood pressure. Future research should explore extended, rigorous physical activity protocols for greater cardiovascular risk reduction. [ABSTRACT FROM AUTHOR]

Background: To investigate the correlation between retinal vascular changes and ICA stenosis by measuring retinal vessels using full-width-at-half-maximum (FWHM) and intelligent image recognition. Methods: This research selected patients who were admitted to the Vascular Surgery Department of Quzhou People's Hospital from January 2018 to December 2020 and were preparing for Carotid Artery Stenting (CAS). Participants were divided into two groups: without ICA stenosis (Group 0) and with ICA stenosis (Group 1). A total of 109 cases were included in the study, with 50 cases in Group 1 and 59 cases in Group 0. Vascular images of superior temporal zone B of the retina were obtained by spectral domain optical coherence tomography (SD-OCT). The edges of retinal vessels were identified by FWHM. Each vessel of all subjects was measured three times with the FWHM, and the average value was taken to obtain the retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), retinal venular lumen diameter (RVLD), and retinal venular outer diameter (RVOD),Arterial Wall Thickness (AWT), Venular Wall Thickness (VWT)=(RVOD-RVLD)/2, Arteriovenous Ratio (AVR) = RAOD/RVOD. Results: We found that compared to Group 0, Group 1 had smaller RALD (P < 0.001) and RAOD (P < 0.001), and wider RVOD (P < 0.001), with thicker VWT (P < 0.001). When compared with the contralateral eye in Group 1, the ipsilateral eye exhibited even smaller RALD, RAOD and AVR (P < 0.001, P < 0.001, P < 0.001). After CAS, the RALD, RAOD and AVR in Group 1 increased (P < 0.001, P < 0.001, P < 0.001),while the RVLD and RVOD decreased (P < 0.05, P < 0.001). Our research reveals a significant correlation between retinal vascular changes and internal ICA stenosis. Conclusion: Utilizing SD-OCT in conjunction with the FWHM, we achieved a non-invasive, intelligent, stable, and precise acquisition of data pertaining to retinal vessels. These findings underscore a significant correlation between alterations in retinal vascular structure and the presence of ICA stenosis, as demonstrated by our research. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) refers to a micro-vascular complication causing vision problems in diabetic patients. It is a serious global public health issue that damages the retinal blood vessels and leaves a large number of patients with loss of vision. Micro-aneurysms, hemorrhages, macular edema, and exudates are some of the lesions used to diagnose it. When detected early, Diabetic Retinopathy can be treated well with precise identification of lesions. Computer-aided diagnosis refers to the approaches used to extract specific features associated with the disease by using image processing filters. Some Machine Learning tools, especially the ones well-suited to data analysis applications are particularly useful. This paper primarily focuses on a comparative performance evaluation of many machine learning and deep learning-based techniques applied for the diagnosis of this vision ailment in diabetic patients. [ABSTRACT FROM AUTHOR]

Retinopathy of prematurity (ROP) is a severe retinal disease in premature infants characterized by pathological neovascularization, obliteration of retinal vessels and increased vessel tortuosity. Currently, there are no completely satisfactory treatments for ROP. Pigment epithelium‐derived factor (PEDF), a potent inhibitor of angiogenesis, appears late in gestation and its deficiency may be linked to development of ROP. This study investigates the preclinical efficacy of PEDF protein alone or in combination with VEGF antagonists for treating ROP. The safety of PEDF protein in the rat eye was assessed using functional in vivo measurements and histology. The efficacy of intravitreal injections (IVI) of various treatments was evaluated in a rat oxygen‐induced retinopathy (OIR) model using in vivo imaging and flatmount analyses. No functional or histological side‐effects were found in rat eyes after intravitreal PEDF protein injection. PEDF protein alone or combined with anti‐VEGF drugs significantly reduced pathological neovascularization and vessel obliteration, comparable to the effects of anti‐VEGF drugs alone. Regarding arterial tortuosity, treatment with a combination of PEDF, and VEGF antagonist was more effective than treatment with anti‐VEGF alone. IVI of PEDF protein is safe. PEDF protein alone or combined with VEGF antagonists shows similar efficacy in reducing pathological neovascularization and vessel obliteration as anti‐VEGF agents. Furthermore, only treatments involving PEDF protein, alone or with VEGF antagonists, significantly improved the quality of retinal vasculature. Thus, PEDF protein alone or combined with anti‐VEGF agents presents a promising alternative to current anti‐VEGF treatments for ROP. [ABSTRACT FROM AUTHOR]

In medical image analysis, precise retinal vessel segmentation is crucial for diagnosing and managing ocular diseases as the retinal vascular network reflects numerous health indicators. Despite decades of development, challenges such as intricate textures, vascular ruptures, and undetected areas persist, particularly in accurately segmenting small vessels and addressing low contrast in imaging. This study introduces a novel segmentation approach called MPCCN that combines position-aware cyclic convolution (PCC) with multi-scale resolution input to tackle these challenges. By integrating standard convolution with PCC, MPCCN effectively captures both global and local features. A multi-scale input module enhances feature extraction, while a weighted-shared residual and guided attention module minimizes background noise and emphasizes vascular structures. Our approach achieves sensitivity values of 98.87%, 99.17%, and 98.88%; specificity values of 98.93%, 97.25%, and 99.20%; accuracy scores of 97.38%, 97.85%, and 97.75%; and AUC values of 98.90%, 99.15%, and 99.05% on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. In addition, it records F1 scores of 90.93%, 91.00%, and 90.55%. Experimental results demonstrate that our method outperforms existing techniques, especially in detecting small vessels. [ABSTRACT FROM AUTHOR]

Automatic segmentation of the fundus retinal vessels and accurate classification of the arterial and venous vessels play an important role in clinical diagnosis. This article proposes a fundus retinal vascular segmentation and arteriovenous classification network that combines the adversarial training and attention mechanism to address the issues of fundus retinal arteriovenous classification error and ambiguous segmentation of fine blood vessels. It consists of three core components: discriminator, generator, and segmenter. In order to address the domain shift issue, U‐Net is employed as a discriminator, and data samples for arterial and venous vessels are generated with a generator using an unsupervised domain adaption (UDA) approach. The classification of retinal arterial and venous vessels (A/V) as well as the segmentation of fine vessels is improved by adding a self‐attention mechanism to improve attention to vessel edge features and the terminal fine vessels. Non‐strided convolution and non‐pooled downsampling methods are also used to avoid losing fine‐grained information and learning less effective feature representations. The performance of multi‐class blood vessel segmentation is as follows, per test results on the DRIVE dataset: F1‐score (F1) has a value of 0.7496 and an accuracy of 0.9820. The accuracy of A/V categorization has increased by 1.35% when compared to AU‐Net. The outcomes demonstrate that by enhancing the baseline U‐Net, the strategy we suggested enhances the automated classification and segmentation of blood vessels. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationship between the fundus sex index obtained from fundus photographs and body height or axial length in the Kumejima population. Study Design: Prospective cross-sectional observational population study. Methods: Using color fundus photographs obtained from the Kumejima population, 1,653 healthy right eyes with reliable fundus parameter measurements were included in this study. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in the eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. The masculine or feminine fundus was quantified using machine learning (L2 regularized binominal logistic regression and leave one out cross validation), with the range of 0–1 as the predictive value, and defined as the fundus sex index. The relationship between the fundus sex index and body height or axial length was investigated using Spearman's correlation. Results: The mean age of the 838 men and 815 women included in this study was 52.8 and 54.0 years, respectively. The correlation coefficient between fundus sex index and body height was − 0.40 (p < 0.001) in all, 0.01 (p = 0.89) in men, and − 0.04 (p = 0.30) in women, and that between fundus sex index and axial length was − 0.23 (p < 0.001) in all, − 0.12 (p < 0.001) in men, and − 0.13 (p < 0.001) in women. Conclusion: This study shows that a larger number of masculine fundi tend to have longer axial lengths in each sex group. However, sex index was not significantly related with body height either in men or in women. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively analyze choroidal and retinal vascular changes in HLA-B27-associated anterior uveitis. Study Design: A retrospective study. Methods: Medical records of 51 eyes with unilateral HLA-B27-associated anterior uveitis, their fellow eyes and 47 sex and age-matched healthy eyes were retrospectively reviewed. Their choroidal and retinal vasculature were analyzed using swept-source (SS) optical coherence tomography (OCT) and OCT angiography (OCTA) scans. Results: Deep capillary plexus (DCP) vessel density (VD) (p < 0.001), choroidal vascularity index (CVI) (p = 0.012), and choriocapillary flow deficit (CCFD) (p < 0.001) of uveitic and fellow eye group were significantly higher than those of control group. On the contrary, superficial capillary plexus (SCP) VD (p < 0.001) of uveitic and fellow eye group were significantly lower than of control group. The vascular parameters of uveitis and fellow eye group showed no significant difference between uveitic and resolution period. Conclusion: Certain choroidal and retinal vascular parameters were significantly changed in both HLA-B27-associated anterior uveitis without posterior segment involvement and the quiet fellow eyes, suggesting their possible effects as a systemic inflammatory disorder. [ABSTRACT FROM AUTHOR]

In patients with Alzheimer's disease (AD) and in animal models, the increased accumulation of amyloid β (Aβ) in retinal blood vessels strongly correlates with brain amyloid deposits and cognitive decline. The accumulation of Aβ in blood vessels may result from impaired transcytosis and a dysfunctional ocular glymphatic system in AD. High-dose fish oil (FO) supplementation has been shown to significantly change the expression of major facilitator superfamily domain-containing protein 2a (Mfsd2a), a key regulator of transcytosis, and Aquaporin 4 (Aqp4), an essential component of the glymphatic system in the retinas of WT mice. We examined the expression of Mfsd2a and Aqp4 in the retinas of 4-month-old 5xFAD female mice supplemented with high-dose FO for three weeks. There was a significant increase in Mfsd2a expression in 5xFAD retinas supplemented with FO compared to control 5xFAD mice. Additionally, the increase in Aqp4 expression observed in 4-month-old 5xFAD retinas, indicative of an impaired glymphatic system, was significantly decreased. Simultaneously, Aβ accumulation in 5xFAD retinal blood vessels was reduced following FO supplementation. These findings suggest that high-dose FO supplementation could serve as an adjunct in developing new treatments aimed at improving the regulation of transcytosis or the function of the glymphatic system in the AD retina. [ABSTRACT FROM AUTHOR]

Background/Objectives: The purpose of this study was to analyze relevant areas in acute-stage fluorescein angiography (FA) images, predicting the long-term visual prognosis of branch retinal vein occlusion (BRVO) based on gradient-weighted class activation mapping (Grad-CAM). Methods: This retrospective observational study included 136 eyes with BRVO that were followed up for more than a year post-FA. Cropped grayscale images centered on the fovea (200 × 200 pixels) were manually pre-processed from early-phase FA at the acute phase. Pairs of the cropped FA images and the best-corrected visual acuity (BCVA) in remission at least one year post-FA were used to train a 38-layer ResNet with five-fold cross-validation. Correlations between the ResNet-predicted and true (actually measured) logMAR BCVAs in remission, and between the foveal avascular zone (FAZ) area measured by ImageJ (version 1.52r) from FA images and true logMAR BCVA in remission were evaluated. The heat maps generated by Grad-CAM were evaluated to determine which areas were consumed as computational resources for BCVA prediction. Results: The correlation coefficient between the predicted and true logMAR BCVAs in remission was 0.47, and that between the acute-stage FAZ area and true logMAR BCVA in remission was 0.42 (p < 0.0001 for both). The Grad-CAM-generated heat maps showed that retinal vessels adjacent to the FAZ and the FAZ per se had high selectivity (95.7% and 62.2%, respectively). Conclusions: The Grad-CAM-based analysis demonstrated FAZ-neighboring vessels as the most relevant predictor for the long-term visual prognosis of BRVO. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting various organs. Ocular involvement, particularly retinopathy, is common, emphasizing the significance of early detection. Optical coherence tomography angiography (OCTA), a non-invasive imaging technique, reveals microvascular changes, aiding SLE diagnosis and monitoring. This study evaluates OCTA's effectiveness in detecting SLE-related retinal alterations. A systemic search was undertaken across PubMed, Embase, and Scopus databases to identify studies presenting OCTA measurements in SLE patients compared to healthy controls. The meta-analysis, employing either fixed-effects or random-effects models based on heterogeneity levels, was conducted. Additionally, subgroup and sensitivity analyses, meta-regression, and quality assessments were carried out. Thirteen studies of 565 eyes in the SLE group and 560 eyes in the control group were included. The meta-analyses revealed that SLE patients had a significantly lower retinal vessel density in the superficial and deep capillary plexus layers, choriocapillaris flow area, and foveal avascular zone (FAZ) circularity index compared to healthy controls, but that there were no significant differences in the FAZ area and perimeter. These findings highlight how OCTA can provide a noninvasive assessment of SLE effects on the retinal microvasculature, potentially presenting a reliable biomarker for more precise detection of SLE and disease activity monitoring. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the association between pretreatment blood flow velocity in the choroid and optic nerve head (ONH) and retinal oxygen metabolism in the acute uveitic phase and the development of 'sunset glow fundus' in Vogt‐Koyanagi‐Harada (VKH) disease. Methods: Retrospective analysis of 41 patients (82 eyes). Laser speckle flowgraphy and retinal oximetry measurements were performed at the presentation. The main outcome measure was the development of 'sunset glow fundus'. Results: Twenty patients (40 eyes) presented in the phase preceding anterior segment inflammation (early presentation), and 21 patients (42 eyes) presented with anterior segment inflammation (late presentation). In ONH, mean blur rate (MBR)‐vessel, representing blood flow velocity in retinal vessels, was significantly lower in the late presentation group, while choroidal MBR was not significantly different. The late presentation group had significantly lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with the early presentation group. Eyes that subsequently developed 'sunset glow fundus' had significantly lower ONH MBR‐vessels, lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with eyes without 'sunset glow fundus'. ONH MBR‐vessel had a significant negative correlation with arteriovenous oxygen saturation difference and a significant positive correlation with calibre of retinal arterioles. Conclusions: In the acute uveitic phase of VKH disease, the development of 'sunset glow fundus' is associated with pretreatment reduced retinal blood flow velocity, calibre of retinal arterioles and oxygen saturation in retinal venules, as well as an increased arteriovenous oxygen saturation difference. [ABSTRACT FROM AUTHOR]

Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results is very challenging due to the diverse structures of retinal tissues, the complex spatial structures of blood vessels, and the presence of many small ships. In recent years, deep learning networks like UNet have been widely used in medical image processing. However, the continuous down-sampling operations in UNet can result in the loss of a significant amount of information. Although skip connections between the encoder and decoder can help address this issue, the encoder features still contain a large amount of irrelevant information that cannot be efficiently utilized by the decoder. To alleviate the irrelevant information, this paper proposes a feature selection module between the decoder and encoder that utilizes the self-attention mechanism of transformers to accurately and efficiently select the relevant encoder features for the decoder. Additionally, a lightweight Residual Global Context module is proposed to obtain dense global contextual information and establish dependencies between pixels, which can effectively preserve vascular details and segment small vessels accurately and continuously. Experimental results on three publicly available color fundus image datasets (DRIVE, CHASE, and STARE) demonstrate that the proposed algorithm outperforms existing methods in terms of both performance metrics and visual quality. [ABSTRACT FROM AUTHOR]

The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications. Methods: Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed. Results: Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses (P< 0.01). There existed no significant difference in blood pressure, body mass index (BMI), blood lipids (triglycerides, cholesterol, low-density lipoprotein, high-density lipoprotein) between the two groups (P 0.05). Compared with the simple diabetes group, patients in the DR group had much higher fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting insulin (FINS), insulin resistance (HOMA-IR) index, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1) (P< 0.001). Besides, the DR group had sharply lower scores on the Mini-Mental State Examination (MMSE) scale and higher levels of the Trail Making Test-A (TMT-A) and TMT-B (P< 0.001). Spearman correlation analysis confirmed that the mean RNFLT was negatively correlated with the levels of FBG, HbA1c, HOMA-IR index, TMT-A and TMT-B (P< 0.05), positively correlated with the score of mini-mental state examination (MMSE) (P< 0.05), and was no significant correlation with FINS and ApoB/ApoA1 (P 0.05). Conclusion: DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE. [ABSTRACT FROM AUTHOR]

Glaucoma, a leading cause of permanent blindness worldwide, necessitates early detection to prevent vision loss, a task that is challenging and time-consuming when performed manually. This study proposes an automatic glaucoma detection method on enhanced retinal images using deep learning. The system analyzes retinal images, generating masks for the optic disc and optic cup, and providing a classification for glaucoma diagnosis. We employ a U-Net architecture with a pretrained residual neural network (ResNet34) for segmentation and an EfficientNetB0 for classification. The proposed framework is tested on publicly available datasets, including ORIGA, REFUGE, RIM-ONE DL, and HRF. Our work evaluated the U-Net model with five pretrained backbones (ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0) and examined preprocessing effects. We optimized model training with limited data using transfer learning and data augmentation techniques. The segmentation model achieves a mean intersection over union (mIoU) value of 0.98. The classification model shows remarkable performance with 99.9% training and 100% testing accuracy on ORIGA, 99.9% training and 99% testing accuracy on RIM-ONE DL, and 98% training and 100% testing accuracy on HRF. The proposed model outperforms related works and demonstrates potential for accurate glaucoma classification and detection tasks. [ABSTRACT FROM AUTHOR]

In the rapidly advancing field of medical image analysis, accurate and efficient segmentation of retinal vessels is paramount for diagnosing ocular diseases, especially diabetic retinopathy. With the increasing emphasis on environmental sustainability, this paper presents BranchFusionNet, a novel lightweight neural network architecture tailored for retinal vessel segmentation. Embodying the principles of energy conservation, BranchFusionNet integrates multi-branch and lightweight dual-branch modules to optimize computational demands without sacrificing segmentation precision. This study not only contributes to the domain of retinal vessel segmentation but also showcases the potential of crafting energy-conscious deep learning methodologies in medical imaging applications. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of topically applied 1% tropicamide, 2.5% phenylephrine and 1% cyclopentolate on retinal vessel calliper (VC) using optical coherence tomography (OCT). Methods: Patients who came to the ophthalmology clinic for routine examination and whose OCT films were taken before dilatation and after 30 min of last dilatation drop were included in the study. 90 ophthalmologically healthy subjects were divided into 3 groups of 30 subject each according to the application of the drops as follows: Tropicamide group (Group 1), Phenylephrine group (Group 2), Cyclopentolate group (Group 3). The right eyes of the subjects were dilated with drops and the left eyes were taken as the control group. VC of retinal artery and vein passing through an area one-half to one-disc diameter from the optic disc margin were measured from OCT films. The mean of the sum of superior retinal artery (SRA) and inferior retinal artery (IRA) VC and the mean of the sum of superior retinal vein (SRV) and inferior retinal vein (IRV) VC before and after the drop were compared. Results: There was no statistically significant change in the mean sum of SRA and IRA VC and the mean sum of SRV and IRV VC before and after dilatation drops in all three groups. Conclusion: Dilatation drops have no statistically significant effect on retinal artery and vein VC. [ABSTRACT FROM AUTHOR]

Abstract Accurate segmentation of retinal blood vessels is crucial for enhancing diagnostic efficiency and preventing disease progression. However, the small size and complex structure of retinal blood vessels, coupled with low contrast in corresponding fundus images, pose significant challenges for this task. We propose a novel approach for retinal vessel segmentation, which combines the transformer and convolutional dual-path decoding U-Net (TCDDU-Net). We propose the selective dense connection swin transformer block, which converts the input feature map into patches, introduces MLPs to generate probabilities, and performs selective fusion at different stages. This structure forms a dense connection framework, enabling the capture of long-distance dependencies and effective fusion of features across different stages. The subsequent stage involves the design of the background decoder, which utilizes deformable convolution to learn the background information of retinal vessels by treating them as segmentation objects. This is then combined with the foreground decoder to form a dual-path decoding U-Net. Finally, the foreground segmentation results and the processed background segmentation results are fused to obtain the final retinal vessel segmentation map. To evaluate the effectiveness of our method, we performed experiments on the DRIVE, STARE, and CHASE datasets for retinal vessel segmentation. Experimental results show that the segmentation accuracies of our algorithms are 96.98, 97.40, and 97.23, and the AUC metrics are 98.68, 98.56, and 98.50, respectively.In addition, we evaluated our methods using F1 score, specificity, and sensitivity metrics. Through a comparative analysis, we found that our proposed TCDDU-Net method effectively improves retinal vessel segmentation performance and achieves impressive results on multiple datasets compared to existing methods.

Bipolar Disorder (BD) is an important psychiatric disease that progresses with attacks, can be chronic and causes serious mental problems. In this study, we aimed to identify the retinal vascular pathologies in BD patients by optical coherence tomography angiography (OCTA) imaging. Retinal vascular analysis from 35 BD patients and 30 healthy controls (HCs) were scanned using the OCTA machine. In addition, psychometric tests such as the Young Mania Rating Scale (YMRS) and Clinical Global Impression Scale (CGI-S) were applied to BD patients to assess the severity of the disease and determine the patient's level of functionality. As a result of OCTA scans, there were significant differences between the groups as following; Deep retinal vessel density (VD) in the total, parafoveal and perifoveal area, Macular thickness in the inner parafoveal area, VD of retinal capillary plexuses in the inside disk and the Choroidal thickness (p < 0.05). Furthermore, according to the results of Pearson correlation analysis between OCTA scans and YMRS and CGI-S scores, it was determined that there was no significant relationship in any measurement (p > 0.05). In our study, it was determined that there were general differences in deep retinal vascular density and inner macular thickness in BD patients. These findings demonstrate that the deep and inner zone of the retina is affected in BD patients. • We investigated changes in retinal vascularity in bipolar disorder (BD). • OCTA measurements performed in BD patients showed that deep and inner areas of the retina were affected. • Retinal findings in BD patients by OCTA, can be used as a new biomarker in the diagnosis and the follow-up of the disease. [ABSTRACT FROM AUTHOR]