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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]

Retinal image segmentation process deals with problems like spurious vascularisation and thin vessel detection. In this paper, a three-step methodology has been proposed for retinal vessel segmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant Herding Optimization (CWEHO) has been proposed to optimize the clip limit and block size values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhance retinal images. In third step, thin and thick vessel segmentation is performed. Top hat transformation, otsu thresholding algorithm, and vessel point selection are applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the match filter is used to extract thin vessels. DRIVE and HRF datasets are used to assess the effectiveness of proposed methodology. The average values of segmentation accuracy, specificity, sensitivity, and Mathew Correlation Coefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650, respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2 using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116, respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology provides better results. [ABSTRACT FROM AUTHOR]

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information. [ABSTRACT FROM AUTHOR]

Background: To explore the functional and morphological variations of retinal vessels in diabetes with no clinically detectable retinopathy (NDR) and mild non-proliferative diabetic retinopathy (NPDR) and to establish a high-performance mild NPDR diagnostic model. Methods: Normal subjects and type 2 diabetes patients with NDR and mild NPDR were recruited. Oxygen-saturation-related functional parameter (optical density ratio ODR) and morphological characteristics (fractal dimension Df, vessel area rate VAR, mean vascular diameter Dm, vessel tortuosity τ) of different vascular areas were extracted with single fundus photography and comprehensively analyzed among groups. An interpretable model combining marine predator algorithm (MPA) and support vector machine (SVM) based on characteristic selection was proposed for mild NPDR diagnosis. Results: A total of 91 NDR subjects, 75 mild NPDR subjects, and 111 sex- and age-matched normal controls were analyzed. Increased main vessels ODR, while lower VAR of all areas except outer ring macula, lower Dm of all vessels and decreased τ of all areas were associate with NDR (e.g. main vessels ODR: OR [95%CI] 1.42[1.07–1.89], full macula τ:0.53[0.38–0.74]). Increased ODR of all areas, higher Dm of all areas except inner ring macula, increased inner ring macula τ, while decreased Df of full and inner ring macula, lower VAR of all areas were associate with mild NPDR (e.g. main vessels ODR:5.68[3.03–10.65], inner ring macula VAR: 0.48[0.33–0.69]). The MPA-SVM model with selected characteristics obtained the best diagnosis performance (AUC:0.940 ± 0.014; Accuracy:90.4 ± 3.9%; Sensitivity:89.2 ± 6.4%; Specificity:91.3 ± 6.4%). Conclusions: More significant retinal vascular variations are associate with the incidence of mild NPDR than NDR. High-precision mild NPDR diagnosis is achieved combining the morphological and functional vascular characteristics based on characteristic selection. [ABSTRACT FROM AUTHOR]

Mesoscopic photoacoustic imaging (PAI) enables label‐free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time‐consuming and error‐prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human‐annotated ground‐truth labels. To address this, an unsupervised image‐to‐image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN‐GAN). VAN‐GAN integrates synthetic blood vessel networks that closely resemble real‐life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient‐derived breast cancer xenograft models and 3D clinical angiograms, VAN‐GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN‐GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high‐quality blood vessel segmentation (F1 score: VAN‐GAN vs. U‐Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the severe complications of diabetes which if not timely diagnosed and treated, can lead to severe retinal damage including irreversible vision loss. The overburdened healthcare system, lack of skilled ophthalmologists, and long delays in reporting the results of fundus images are the main reasons for untimely intervention. AI-based diagnostic systems can improve the situation. In fact, World Economic Forum has listed AI-facilitated healthcare as one of the leading emerging technologies. Many deep learning algorithms have been proposed for the diagnosis of DR in recent years but these studies only considered one or two symptoms for detecting diabetic retinopathy such as exudates, vessels, or hemorrhages, and not all of them together. In this paper, we propose a diagnostic system that takes into account all symptoms for detecting diabetic retinopathy and proposed a novel retinal vessels extraction algorithms that overcame the other studies vessel extraction algorithm. In this study, we also classify abnormal images as having exudates or not. For this purpose, fundus image datasets were annotated by experts for the presence or absence of exudates, these retinal images are then passed through pre-processing stage for extraction of haemorrhages and retinal vessels separately, these pre-processed images are then fused to one image and passed to two classifier, one classifier trained on normal and abnormal retinal images and second classifier trained to classify DR due to other symptoms (haemorrhages/neovascularization) and DR due to exudates. These classifiers are arranged in a cascaded way for identifying image with no DR, DR with exudates and DR with other symptoms. Results show that the proposed extraction algorithms improve the performance of the state-of-the-art DR classification model. Our model achieved 0.88 accuracy, with 0.91 precision, 0.86 recall, and 0.88 F1 score. [ABSTRACT FROM AUTHOR]

Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a common eye disease that results in vision loss by damaging the blood vessels. Diabetic patients are at high risk of developing DR owing to the damage of retinal lesions, thereby causing clots, injuries and bleeding. The disease leads to abnormal changes in the structure of the retina. Therefore, the timely detection and early treatment of eye diseases are essential for preventing people from vision loss. Ophthalmologists distinguish DR based on features such as exudes, microaneurysms, blood vessel area, hemorrhages, etc. An artificial intelligence (AI) method is proposed by ensembling a deep learning (DL) model with the explainable AI based Shapley additive (SHAP) method for DR image segmentation and classification. The proposed model uses fundus images to detect abnormalities in the eye. First, the DR images are collected from the Asia Pacific Tele-Ophthalmology Society 2019 blindness detection (APTOS 2019) dataset. Data augmentation is performed to artificially increase the size of a training dataset by generating new data samples from existing ones by rescaling, flipping, rotating, zooming, etc. In order to improve the quality and enhance specific features, pre-processing is performed. The pre-processed images are segmented using the improved U-Net model, where the severity of the disease gets predicted, and the fundus images are segmented using the trained model to attain precise abstraction of retinal blood vessels. Finally, the proposed study used the Shapley Additive Ensembled DenseNet-121 ResNet-50 (SAE-DR) model to detect DR disease based on the features. To improve the readability of the deep learning model, an explainable AI based Shapley additive method is proposed. Then, compare the results of the proposed model with existing state-of-the-art methods. The simulation results prove that the proposed model achieves superior detection performance with an accuracy of 98.69%, sensistivity of 86.23%, specificity of 97.54%, F-score of 90.26%, precision of 94.26% and processing time of 0.153 s. [ABSTRACT FROM AUTHOR]

Globally, eye disorders have been a major issue, especially in developing nations where resources for technology and financing are limited. Due to its tremendous feature learning ability, CNN has obtained remarkable progress in the area of fundus images. Through appropriate analysis and examination of fundus images, computer-aided diagnosis may yield information with a standard value for experts in clinical diagnosis or screening. However, the majority of earlier investigations have focused on the identification of a particular fundus disease, and the accurate fast classification of multiple fundus diseases remains a major challenge. Extremely numerous retinal fundus images must be analyzed to achieve a categorization that is reliable, fast, and precise. As a result, this research aims to propose a novel classification model for eye illnesses based on four major steps: (a) Pre-processing, (b) segmentation of blood vessels, (c) Extracting features and (d) Multi-classification of eye disease. In order to accurately classify the images, it is necessary to extract the essential informational characteristics from the segmented blood vessels. The categorization of eye diseases is carried out using a hybrid classifier that integrates the SqueezeNet and Long-Term Recurrent Convolutional Network (LRCN). We trained and validated our models using tenfold cross-validation tests on a database of fundus images including five categories: Normal, Retinitis Pigmentosa, Pathological Myopia, Maculopathy and Glaucoma. The efficacy of the presented approach is evaluated based on specificity, F1-score, recall, precision and accuracy of 98%, 97.8%, 97.6%, 98.4% and 98% respectively. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to evaluate macular microvascular changes and their correlation with visual outcomes after idiopathic epiretinal membrane (iERM) surgery. Methods: Forty-seven eyes operated for iERM were included in this retrospective case series. The foveal avascular zone (FAZ) area, and the vessel density (VD) in the superficial and the deep capillary plexus (SCP and DCP) were evaluated using optical coherence tomography angiography (OCTA). The association between the OCTA parameters and best-corrected visual acuity (BCVA) was examined preoperatively and postoperatively. Regression analyses were conducted to determine the potential predictive factors for visual recovery. Results: At baseline, the FAZ area in iERM eyes was significantly smaller than that in the control eyes (p < 0.001). iERM eyes also had a lower macular VD in both the SCP and the DCP (p < 0.001). Preoperative BCVA was negatively correlated with the FAZ area (r = −0.499, p < 0.001) and macular VD in the DCP (r = −0.422, p = 0.003). A negative correlation was also found between postoperative BCVA and macular VD in both the SCP (r = −0.394, p = 0.006) and the DCP (r = −0.569, p < 0.001). In the bivariate analyses, age, preoperative BCVA, iERM stage, and baseline macular VD in the SCP were significantly associated with BCVA at 6 months post-surgery. Multivariate regression analysis revealed that the preoperative BCVA was the only predictor of visual outcomes in iERM eyes (p < 0.001). Conclusions: Idiopathic epiretinal membrane (iERM) causes microvascular changes, including foveal avascular zone (FAZ) contraction and decreased macular vessel density (VD) in both the superficial capillary plexus (SCP) and the deep capillary plexus (DCP). These changes were significantly correlated with pre- and/or postoperative best-corrected visual acuity (BCVA). However, none of these alterations appeared to have prognostic value for visual outcomes in patients with iERM. [ABSTRACT FROM AUTHOR]

It has been reported that the retinal vessel and macular region of the retina are displaced after macular hole (MH) surgery. However, there is no detailed information for correlations between retinal and choroidal displacements. We obtained optical coherence tomography angiography (OCTA) and en-face optical coherence tomography (OCT) images from 24 eyes to measure the retinal and choroidal vascular displacement before and after surgery. These images were merged into infrared images using blood vessel patterns. The same vascular bifurcation points were automatically selected for each follow-up image, and the displacements of the bifurcation points were analyzed as a vector unit for prespecified grid regions in a semi-automated fashion. The results showed displacements of the choroidal intermediate vessels and retinal vessels following MH surgery (p = 0.002, p < 0.001). The topographic changes showed inferior, nasal, and centripetal displacement of the retina and inferiorly displaced choroid. The ILM peeling size and basal MH size were significantly associated with the retinal displacement (p < 0.001 and p = 0.010). Additionally, changes in the amount of the choroidal displacement were significantly correlated with that of the retinal displacements (p = 0.002). Clinicians should keep in mind that there might be topographic discrepancies of the displacement between retina and choroid when analyzing them following surgery. [ABSTRACT FROM AUTHOR]

Acute pancreatitis, a common exocrine inflammatory disease affecting the pancreas, is characterized by intense abdominal pain and multiple organ dysfunction. However, the alterations in retinal blood vessels among individuals with acute pancreatitis remain poorly understood. This study employed optical coherence tomography angiography (OCTA) to examine the superficial and deep retinal blood vessels in patients with pancreatitis. Sixteen patients diagnosed with pancreatitis (32 eyes) and 16 healthy controls (32 eyes) were recruited from the First Affiliated Hospital of Nanchang University for participation in the study. Various ophthalmic parameters, such as visual acuity, intraocular pressure, and OCTA image for retina consisting of the superficial retinal layer (SRL) and the deep retinal layer (DRL), were recorded for each eye. The study observed the superficial and deep retinal microvascular ring (MIR), macrovascular ring (MAR), and total microvessels (TMI) were observed. Changes in retinal vascular density in the macula through annular partitioning (C1–C6), hemispheric quadrant partitioning (SR, SL, IL, and IR), and early diabetic retinopathy treatment studies (ETDRS) partitioning methods (R, S, L, and I). Correlation analysis was employed to investigate the relationship between retinal capillary density and clinical indicators. Our study revealed that in the superficial retinal layer, the vascular density of TMI, MIR, MAR, SR, IR, S, C2, C3 regions were significantly decreased in patients group compared with the normal group. For the deep retinal layer, the vascular density of MIR, SR, S, C1, C2 regions also reduced in patient group. The ROC analysis demonstrated that OCTA possesses significant diagnostic performance for pancreatitis. In conclusion, patients with pancreatitis may have retinal microvascular dysfunction, and OCTA can be a valuable tool for detecting alterations in ocular microcirculation in pancreatitis patients in clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Neurovascular degeneration results in vascular dysfunction, leakage, ischemia, and structural changes that can lead to significant visual impairment. We previously showed the protective effects of peptain-1, a 20 amino acid peptide derived from the aB-crystallin core domain, on retinal ganglion cells in two animal models of glaucoma. Here, we evaluated the ability of peptain-1 to block apoptosis of human retinal endothelial cells (HRECs) in vitro and retinal capillary degeneration in mice subjected to retinal ischemia/reperfusion (I/R) injury. Methods: HRECs were treated with either peptain-1 or scrambled peptides (200 mg/mL) for 3 h and a combination of proinflammatory cytokines (IFN-g 20 ng/mL + TNF-a 20 ng/mL+ IL-1b 20 ng/mL) for additional 48 h. Apoptosis was measured with cleaved caspase-3 formation via western blot, and by TUNEL assay. C57BL/6J mice (12 weeks old) were subjected to I/R injury by elevating the intraocular pressure to 120 mmHg for 60 min, followed by reperfusion. Peptain-1 or scrambled peptide (0.5 mg) was intravitreally injected immediately after I/R injury and 7 days later. One microliter of PBS was injected as vehicle control, and animals were euthanized on day 14 post-I/R injury. Retinal capillary degeneration was assessed after enzyme digestion followed by periodic acid-Schiff staining. Results: Our data showed that peptain-1 entered HRECs and blocked proinflammatory cytokine-mediated apoptosis. Intravitreally administered peptain-1 was distributed throughout the retinal vessels after 4 h. I/R injury caused retinal capillary degeneration. Unlike scrambled peptide, peptain-1 protected capillaries against I/R injury. Additionally, peptain-1 inhibited microglial activation and reduced proinflammatory cytokine levels in the retina following I/R injury. Discussion: Our study suggests that peptain-1 could be used as a therapeutic agent to prevent capillary degeneration and neuroinflammation in retinal ischemia. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionsTo reveal changes in choroidal thickness, retinal vessel density, and serum HIF-1α and TNF-α levels in obstructive sleep apnea syndrome (OSAS) and their correlation.This prospective case-control study included 118 patients divided into mild-to-moderate OSAS (n = 40), severe OSAS (n = 39), and a control group (n = 39). Choroidal thickness was evaluated with OCT, vessel density with OCTA, AHI index with polysomnography, and serum HIF-1α and TNF-α levels were analyzed using the enzyme-linked immunosorbent assay.The serum HIF-1α values of the participants in the mild-moderate OSAS and severe OSAS groups were [893.25(406.7–2068) and 1027(453–2527), respectively], and were both significantly higher than the control group [(521.5(231.6–2741))] (p < 0.001). Serum TNF-α levels did not differ significantly between the groups (p = 0.051).). Subfoveal choroidal thickness (SFCT) values of the severe OSAS groups were significantly lower than the control group (p < 0.05). The superficial and deep capillary plexus vascular density (SVD and DVD) values of the severe OSAS group were lower than the control group (p < 0.05). Serum HIF-1α and TNF-α levels of all participants were negatively correlated with both their SVD values (p < 0.05, r: –0.220 and p < 0.05, r: –0.252, respectively) and their DVD values (p < 0.001, r: –0.324 and p = 0.001, r: –0.299, respectively).Increased serum levels of inflammatory mediators (HIF-1α ve TNF-α) in OSAS cause a decrease in SFCT, SVD, and DVD, which is an indication of systemic vascular damage. Further research on developing treatment strategies to modulate TNF-α ve HIF-1α may help recede vascular morbidity in OSAS patients. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we aimed to evaluate and compare the effects of intravitreal aflibercept (IVA) and intravitreal faricimab (IVF) injections on the blood flow of retinal vessels in the peripapillary region and optic nerve head (ONH) in eyes with diabetic macular edema (DME) using laser speckle flowgraphy (LSFG). Patients and Methods: This study included 20 eyes of 18 patients treated with IVA and 15 eyes of 11 patients treated with IVF for DME. The mean blur rate (MBR) of the ONH and retinal artery and vein of the peripapillary region were measured using LSFG at baseline and 1 month after injection. Central retinal thickness (CRT) and best-corrected visual acuity (BCVA) were measured for all patients. Results: CRT decreased significantly in both IVA-treated (p = 0.0003) and IVF-treated groups (p = 0.0004). Some of the MBR-related parameters of the ONH, such as MBR of all areas (MA), MBR of vascular areas (MV), and MBR of tissue areas (MT), decreased significantly 1 month after IVA and IVF compared to baseline values (MA of IVA, p < 0.0001; MT of IVA, p = 0.0220; MA of IVF, p = 0.0002; MT of IVF, p = 0.0461). MBR of the retinal artery (MBR-A) and vein (MBR-V) also decreased significantly 1 month after IVA and IVF compared with baseline values (MBR-A of IVA, p = 0.0002; MBR-V of IVA, p = 0.0010; MBR-A of IVF, p = 0.0368). No significant difference in ocular perfusion was observed between the IVA-treated and IVF-treated groups. Conclusion: Intravitreal injection led to a reduction in ocular blood flow in both retinal peripapillary vessels and the ONH in both IVA-treated and IVF-treated groups. No significant difference was observed in MBR reduction between the IVA-treated and IVF-treated groups. Our findings warrant further long-term investigations to reveal differences between aflibercept and faricimab. [ABSTRACT FROM AUTHOR]

Background Diabetic nephropathy (DN) and diabetic retinopathy (DR) are common microvascular complications of diabetes. The purpose of this study was to investigate the correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 DN and to determine the capacity of retinal vascular geometric parameters in differentiating DN from non-diabetic renal disease (NDRD). Methods The study participants were adult patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease who underwent a renal biopsy. Univariate and multivariable regression analyses were performed to evaluate associations between retinal vessel geometry parameters and pathologically diagnosed DN. Multivariate binary logistic regression analyses were performed to establish a differential diagnostic model for DN. Results In total, 403 patients were examined in this cross-sectional study, including 152 (37.7%) with DN, 157 (39.0%) with NDRD and 94 (23.3%) with DN combined with NDRD. After univariate logistic regression, total vessel fractal dimension, arteriolar fractal dimension and venular fractal dimension were all found to be associated with DN. In multivariate analyses adjusting for age, sex, blood pressure, diabetes, DR and other factors, smaller retinal vascular fractal dimensions were significantly associated with DN (P  < .05). We developed a differential diagnostic model for DN combining traditional clinical indicators and retinal vascular geometric parameters. The area under the curve of the model established by multivariate logistic regression was 0.930. Conclusions Retinal vessel fractal dimension is of great significance for the rapid and non-invasive differentiation of DN. Incorporating retinal vessel fractal dimension into the diagnostic model for DN and NDRD can improve the diagnostic efficiency. [ABSTRACT FROM AUTHOR]

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements. [ABSTRACT FROM AUTHOR]

The article discusses the expanding role of machine learning (ML) in supporting decision-making across various engineering fields, particularly in the biomedical sector. Topics include the use of ML and deep learning (DL) in medical imaging for diagnosing and monitoring diseases such as retinal and liver conditions, the advancements of DL strategies over traditional ML methods in interpreting biomedical images and learning high-level features, and development of intelligent decision systems.

Giant cell arteritis is a challenging diagnosis for patients given the high prevalence of negative temporal artery biopsies (TAB). Despite the lack of histopathological evidence of giant cell arteritis in the TAB, patients can still have TAB-negative giant cell arteritis. The purpose of this paper is to analyse the predictors for TAB-negative giant cell arteritis and the alternative diagnosis of biopsy-negative patients without a giant cell arteritis diagnosis. A retrospective electronic database review of all TABs performed at the Royal Victorian Eye and Ear Hospital from February 2015 to May 2020. Logistic regression analysis was performed to determine predictive factors for a diagnosis of TAB-negative giant cell arteritis. In all cases, a clinical diagnosis of TAB-negative giant cell arteritis was determined by a neuro-ophthalmologist. Alternative diagnoses for negative TABs were identified and explored. A total of 368 TABs were analysed with 287 (78%) negative for histopathological evidence of GCA. Twenty-seven (9.4%) patients were diagnosed and treated as TAB-negative giant cell arteritis. The clinical predictors of a TAB-negative giant cell arteritis diagnosis were the presence of jaw claudication (OR 2.77, 95% CI 1.10–6.98) and CRP (OR 1.02, 95% CI 1.00–1.03). Alternative diagnoses included non-specific headache, non-arteritic anterior ischaemic optic neuropathy, retinal vessel occlusions, and ocular nerve palsies. Predictive factors for a diagnosis of TAB-negative giant cell arteritis were jaw claudication and an elevated CRP. Several alternative diagnoses can be considered for patients with a negative TAB in a neuro-ophthalmology context. [ABSTRACT FROM AUTHOR]

Uveitis comprises a cluster of intraocular inflammatory disorders characterized by uncontrolled autoimmune responses and excessive oxidative stress leading to vision loss worldwide. In the present study, curcumin (CUR) was conjugated with polyvinylpyrrolidone (PVP) to form PVP-CUR nanoparticles with significantly elevated solubility and outstanding multiple radical scavenging abilities. In vitro studies revealed that PVP-CUR nanoparticles markedly mitigated oxidative stress and reduced apoptosis in a H 2 O 2 -induced human retinal pigment epithelial cell line (ARPE-19) and promoted phenotypic polarization from M1 to M2 in an LPS-induced human microglial cell line (HMC3). Further in vivo studies demonstrated the prominent therapeutic effects of PVP-CUR nanoparticles on experimental autoimmune uveitis (EAU), which relieved clinical and pathological progression, improved perfusion and tomographic manifestations of retinal vessels, and reduced blood–retinal barrier (BRB) leakage; these effects may be mediated by mitigating oxidative stress and attenuating macrophage/microglia-elicited inflammation. Notably, treatment with PVP-CUR nanoparticles was shown to regulate metabolite alterations in EAU rats, providing novel insights into the underlying mechanisms involved. Additionally, the PVP-CUR nanoparticles showed great biocompatibility in vivo. In summary, our study revealed that PVP-CUR nanoparticles may serve as effective and safe nanodrugs for treating uveitis and other oxidative stress- and inflammation-related diseases. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Acute macular neuroretinopathy (AMN) can cause sudden-onset and permanent scotoma in healthy young patients. Analysis of optical coherence tomography (OCT) and OCT angiography (OCTA) of AMN patients may provide insights into disease mechanism. Methods: We conducted a retrospective study of consecutive SARS-Cov-2-related AMN patients that presented in our clinic between Jan 1st, 2022, and April 30th, 2023, within 30 days of symptom onset. Retinal vessel area density (VAD) of AMN lesions in OCTA was quantified and compared to an adjacent tissue control (ATC). This quantification was performed for the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP), the deep capillary plexus (DCP), the choriocapillaris (CC), and choroid. Furthermore, en face OCT images were analyzed. Results: Nine AMN patients were identified, 6 of these (4 female, 2 male, average age 25 years) fulfilled the inclusion criteria and were included into this study. Average time from symptom onset to OCTA was 14.3 days. No VAD differences between AMN and adjacent tissue were found in either retinal layer (SVP, ICP, DCP). In contrast, VAD in CC was reduced by 27% against the ATC (p = 0.007) and choroidal VAD was reduced by 41% (p = 0.017). Further analysis of en face OCT could show that the pathognomonic infrared hyporeflectivity in AMN is caused by photoreceptor alterations rather than changes in the inner retinal layers. Conclusions: Our data suggests that a perfusion deficit in the choroidal layers is responsible for AMN rather than in the DCP, which is the predominant hypothesis in current literature. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of death in the world, with radiotherapy as one of the treatment options. Radiotherapy planning starts with delineating the affected area from healthy organs, called organs at risk (OAR). A new approach to automatic OAR segmentation in the chest cavity in Computed Tomography (CT) images is presented. The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder, which is the baseline adopted in this work. The new two‐branch CS‐SA U‐Net architecture is proposed, which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function (CS‐SA) blocks are inserted between the encoder and decoder, which enabled the use of consistency regularisation. The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient (oesophagus—0.8714, heart—0.9516, trachea—0.9286, aorta—0.9510) and Hausdorff distance (oesophagus—0.2541, heart—0.1514, trachea—0.1722, aorta—0.1114) and significantly outperforms the baseline. The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning. [ABSTRACT FROM AUTHOR]

The eyes provide insights into psychology, potentially offering a distinctive perspective for psychological health profiles. However, there exist a notable deficiency in datasets that simultaneously encompass eye features and psychological assessments. To address this gap, our study presents a dataset that included Fundus Photography, Psychological Assessment, Retina Characteristics, and Multimodal Imaging (FPRM). FPRM dataset comprise fundus images at different wavelengths (548 nm and 605 nm), image of oxygen saturation for the retina and 8 specific retinal vessels, videos of retinal blood flow and pupillary light reflex, along with 61 items of multimodal quantitative measurement from 384 participants. Additionally, it features psychological assessments across five dimensions (geriatric depression, generalized anxiety disorder, insomnia, activities of daily living, and deterioration), accompanied by fundus photographs and 6 items of retina characteristics from 1683 participants. FPRM dataset is the first to integrate multimodal ophthalmic data and psychological assessments, not only advancing the development of machine learning applications but also facilitating in-depth research into the relationship between eye health and psychological health profiles. [ABSTRACT FROM AUTHOR]

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To study retinal and choriocapillaris (CC) alterations using optical coherence tomography angiography (OCTA) in long-term type 1 diabetic (DM1) patients without diabetic retinopathy (DR). Seventy-eight eyes from 78 well-controlled DM1 patients diagnosed at least 15 years prior and 130 eyes of 130 healthy subjects were included in a cross-sectional descriptive study. Six eyes were excluded from the DM1 group. OCTA with Deep Range Imaging (DRI)-Triton swept source (SS)-OCT was performed. Statistically significant differences were found in all areas of the superficial capillary plexus (SCP), with lower values in DM1 patients. Differences were noted in all quadrants of the deep capillary plexus (DCP) except for the central area. Significant changes in CC blood flow were only found in the center. The foveal avascular zone (FAZ) area and diameters in the SCP were significantly different, while the DCP FAZ area was similar in both groups. Disease duration and microalbuminuria correlated negatively with some SCP areas and positively with FAZ values. Anatomical evaluation revealed microaneurysms in both plexuses, FAZ modifications, and areas lacking blood perfusion. Long-term type 1 diabetic patients without DR display microvascular abnormalities affecting retinal and CC blood perfusion, along with anatomical changes in retinal blood vessels. [ABSTRACT FROM AUTHOR]

Pathological retinal angiogenesis profoundly impacts visual function in vascular eye diseases, such as retinopathy of prematurity (ROP) in preterm infants and age-related macular degeneration in the elderly. While the involvement of photoreceptors in these diseases is recognized, the underlying mechanisms remain unclear. This study delved into the pivotal role of photoreceptors in regulating abnormal retinal blood vessel growth using an oxygen-induced retinopathy (OIR) mouse model through the c-Fos/A disintegrin and metalloprotease 17 (Adam17) axis. Our findings revealed a significant induction of c-Fos expression in rod photoreceptors, and c-Fos depletion in these cells inhibited pathological neovascularization and reduced blood vessel leakage in the OIR mouse model. Mechanistically, c-Fos directly regulated the transcription of Adam17 a shedding protease responsible for the production of bioactive molecules involved in inflammation, angiogenesis, and cell adhesion and migration. Furthermore, we demonstrated the therapeutic potential by using an adeno-associated virus carrying a rod photoreceptor-specific short hairpin RNA against c-fos which effectively mitigated abnormal retinal blood vessel overgrowth, restored retinal thickness, and improved electroretinographic (ERG) responses. In conclusion, this study highlights the significance of photoreceptor c-Fos in ROP pathology, offering a novel perspective for the treatment of this disease. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy and Glaucoma are two common diabetic retinal pathogens. It has the potential to damage human retina and result in vision impairment. Based on the International Diabetes Federation (IDF) reports, In India 77 million people were affected with diabetics 2019 and approximately 147.2 million people are expected by 2045. By effectively managing diabetes and undergoing routine eye examinations, would avoid or reduce the risk of eye complications such as diabetic retinopathy, cataracts, and Glaucoma. The Timely identification and precise delineation of affected areas in retinal pathogen images are crucial for effective disease management. To address this, we propose a novel hybrid diabetic retinal pathogen classification mechanism using Artificial Fish Swarm and Deep Convolutional Radial Basis Function network (AFS-DCRBF). This method utilizes retinal images containing normal, Diabetic-Retinopathy, and Glaucoma as inputs. Preprocessing of raw input images is performed using a spatial filtering technique in the initial phase. A vector-auto regression method is then employed for feature engineering, followed by retinal pathogen classification. The Deep-Convolutional Neural Network (DCNN) selects most informative traits from the extracted feature sequence, while the Radial Basis Function (RBF) module performs the classification task. The Artificial Fish Swarm (AFS) optimization fine-tunes the hyperparameters of DCRBF and improves classification performance. The proposed study was evaluated using ORIGA data set and publicly available datasets for DR. The proposed method required a total time of 1.12 seconds and achieved 99.40% accuracy and 99.61%specificity, and dice coefficient of 0.97. [ABSTRACT FROM AUTHOR]

Background: CT‐image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time‐consuming process and inter‐observer variations of manual segmentation have limited wider application in clinical practice. Purpose: Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. Methods: This retrospective study was performed to develop a coarse‐to‐fine DL‐based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast‐enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL‐based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. Results: Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL‐segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001). Conclusions: The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images. [ABSTRACT FROM AUTHOR]

Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis. [ABSTRACT FROM AUTHOR]

We aim to observe the posterior scleral reinforcement (PSR) clinical outcomes of children with high myopia and analyze the retinal vessel alteration before and after PSR by using angiography optical coherence tomography (angio-OCT). Fifty-six pediatric participants (112 eyes) clinically diagnosed high myopia were recruited and were treated by PSR in Shanghai Children's Hospital from June 1, 2021 to May 1, 2023. The average age ranged from 5.42 to 14.83 years (mean 8.83 years) and mean follow up duration was 8.7 months (3–24 months). The axial length (AL) was significantly shortened after PSR (p < 0.05). The spherical equivalent (SE) and the best-corrected visual acuity (BCVA) were also improved without severe rejection in the follow-up. Compared with baseline, angio-OCT parafoveal vessel indices including vascular area density (VAD) and vascular skeleton density (VSD) on the superficial capillary plexus layer (SCPL), as well as VAD and vessel perimeter index (VPI) on the deep capillary plexus layer (DCPL), were significantly increased after PSR surgery (p < 0.05). VPI on the SCPL, vascular diameter index (VDI) and VSD on the DCPL were also improved without statistical difference after PSR. The VSD on SCPL, VAD on DCPL of the right eyes and the VPI on SCPL of the left eyes were significantly increased after PSR (p < 0.05). PSR surgery can shorten the AL and can stable BCVA and SE in high myopia children. The angio-OCT parameters indicated that the retinal microcirculation supply was significantly improved after PSR. [ABSTRACT FROM AUTHOR]

This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases. [ABSTRACT FROM AUTHOR]

Loss of the inner blood–retinal barrier (BRB) integrity is a main feature of ocular diseases such as diabetic macular edema. However, there is a lack of clarity on how inner BRB function is modulated within the diabetic retina. The current study examined whether eucalyptol inhibited inner BRB destruction and aberrant retinal angiogenesis in 33 mM glucose-exposed human retinal microvascular endothelial (RVE) cells and db/db mice. This study further examined the molecular mechanisms underlying endothelial dysfunction including retinal endoplasmic reticulum (ER) stress and angiopoietin (Ang)/Tie axis in conjunction with vascular endothelial growth factor (VEGF). Eucalyptol is a naturally occurring monoterpenoid and an achiral aromatic component of many plants including eucalyptus leaves. Nontoxic eucalyptol reduced the production of amyloid-β (Aβ) protein in glucose-loaded RVE cells and in diabetic mice. This natural compound blocked apoptosis of Aβ-exposed RVE cells in diabetic mouse eyes by targeting ER stress via the inhibition of PERK-eIF2α-ATF4-CHOP signaling. Eucalyptol promoted activation of the Ang-1/Tie-2 pathway and dual inhibition of Ang-2/VEGF in Aβ-exposed RVE cells and in diabetic eyes. Supply of eucalyptol reversed the induction of junction proteins in glucose/Aβ-exposed RVE cells within the retina and reduced permeability. In addition, oral administration of eucalyptol reduced vascular leaks in diabetic retinal vessels. Taken together, these findings clearly show that eucalyptol inhibits glucose-induced Aβ-mediated ER stress and manipulates Ang signaling in diabetic retinal vessels, which ultimately blocks abnormal angiogenesis and loss of inner BRB integrity. Therefore, eucalyptol provides new treatment strategies for diabetes-associated RVE defects through modulating diverse therapeutic targets including ER stress, Ang-1/Tie-2 signaling, and Ang-2/VEGF. [ABSTRACT FROM AUTHOR]

Automatic segmentation of retinal vessels plays an important role in computer-aided diagnosis of ophthalmology and cardiovascular diseases. Attention mechanism can improve the efficiency and accuracy of image feature extraction in classical neural network models, so attention mechanism is widely used in retinal vessel segmentation models. This paper firstly reviews the commonly used datasets and evaluation metrics for retinal vessel segmentation, subsequently, attention mechanisms are categorized into two types: selective attention mechanisms and self-attention mechanisms, based on their working principles. Meanwhile, according to the data domain of computer vision tasks, attention methods are divided into three categories: channel attention, spatial attention, and mixed attention. Combined with the task of retinal vessel segmentation, the paper highlights the specific applications of representative attention models of these three types and conducts performance comparisons and evaluations of relevant models. Finally, the problems of attention mechanism and the development trend in the future are discussed. [ABSTRACT FROM AUTHOR]

Depression is a prevalent mental illness, especially major depression, which has a negative impact on individuals and society. In clinical practice, doctors diagnose depression primarily based on self-reported scores, which can be highly subjective. Therefore, developing a framework for diagnosing and identifying depression is a highly significant study. However, existing studies in this field face the challenges of lack of sample size and multimodal data fusion due to difficulties in obtaining patient data. To address these challenges, we propose a multimodal graph neural network-based model for depression detection. In this model, we solve the few-shot learning problem based on a GNN, which can recursively aggregate and transform neighboring nodes to refine the node representation and is very effective for few-shot learning. For multimodal fusion in depression recognition, a pre-fusion strategy is used to fuse three different modal features (audio, text, and video), and input them into the Bi-LSTM fusion network to learn high-level global features of multimodal information to form a multimodal fusion representation. Finally, we embedded the multimodal fusion module into a GNN to predict depression. This study not only solves the multimodal fusion problem but also can effectively improve the generalization performance of few-shot learning. The method achieved an accuracy of 0.861 on the publicly available depression-based dataset DAIC-WOZ, and the final prediction results far exceeded the baseline level, this shows that our model is highly applicable when dealing with small amounts of multimodal medical data. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the preoperative choriocapillaris perfusion (CCP) as a biomarker in patients with idiopathic epiretinal membrane (iERM). Materials and methods: 28 patients (28 eyes) with unilateral iERM who received pars plana vitrectomy (PPV) with internal limiting membrane (ILM) peeling were included for retrospective observational study. Optical coherence tomography (OCT) and angiography (OCTA) was performed before and after PPV. Area, perimeter, and circularity of superficial foveal avascular zone (FAZ) were analyzed preoperatively in both eyes using OCTA. Preoperative CCP was also analyzed with binarized en-face OCTA images. Measurements of best-corrected visual acuity (BCVA) and central foveal thickness (CFT) by OCT were conducted at the baseline and 6 months following the surgery. The correlations of preoperative OCT parameters with postoperative BCVA and CFT reduction were analyzed. Results: CCP was significantly lower (p < 0.001) and FAZ had shrunk (p < 0.001) in eyes with iERM compared to unaffected fellow eyes before surgery. BCVA and CFT became significantly improved after surgery (p = 0.001, p < 0.001). Multiple regression analysis revealed that preoperative CCP was significantly related with BCVA improvement (β = 0.185, p = 0.005), postoperative BCVA (β = 0.108, p = 0.023) and ratio of post- to preoperative CFT (β = 0.106, p = 0.044). Conclusions: Preoperative CCP is a biomarker for poor functional and anatomical prognosis after surgery in iERM. [ABSTRACT FROM AUTHOR]

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications. [ABSTRACT FROM AUTHOR]

Purpose Design Methods Results Conclusion To characterise the retinal vasculometry of a Danish eye and vision cohort and examine associations with systolic blood pressure (BP), diastolic BP, mean arterial BP, and intraocular pressure (IOP).Longitudinal study.The retinal vasculature of fundus images from the FOREVER (Finding Ophthalmic Risks and Evaluating the Value of Eye exams and their predictive Reliability) cohort was analysed using a fully automated image analysis program. Longitudinal associations of retinal vessel morphology at follow‐up visit with IOP (baseline and follow‐up) and BP (follow‐up) were examined using multilevel linear regression models adjusting for age, sex and retinal vasculometry at baseline as fixed effects and person as random effect. Width measurements were additionally adjusted for the spherical equivalent.A total of 2089 subjects (62% female) with a mean age of 61 (standard deviation 8) years and a mean follow‐up period of 4.1 years (SD 0.6 years) were included. The mean arteriolar diameter was approximately 20% thinner than the mean venular diameter, and venules were about 21%–23% less tortuous than arterioles. BP at follow‐up was associated with decreased arteriolar diameter from baseline to follow‐up. After adjusting for baseline IOP, IOP at follow‐up was associated with increased arteriolar tortuosity above baseline (0.59%, 95% CI 0.08–1.10, p‐value 0.024).In a Danish eye and vision cohort, variations in BP and alterations in IOP over time were associated with changes in the width and tortuosity of retinal vessels. Our findings contribute novel insights into retinal vascular alterations over time. [ABSTRACT FROM AUTHOR]

Background: Retinal vasculitis is an inflammatory condition that affects the retinal blood vessels. Summary: It can manifest as an idiopathic disorder or be secondary to various infectious or non-infectious diseases, mimicking syndromes, isolated ocular disorders, or drug-induced reactions. Recognizing its distinctive features is crucial for early diagnosis and accurate treatment. This review aimed to demonstrate the variety of tools available to detect disease activity, assess complications, measure the extent of retinal damage, and guide therapy effectively. Key Message: This review article highlights the use of multimodal imaging in the comprehensive evaluation of retinal vasculitis. [ABSTRACT FROM AUTHOR]

Purpose: This cross-sectional optical coherence tomography angiography (OCTA) study aimed to assess the macular and optic nerve head (ONH) vascular density, foveal avascular zone, and outer retina and choriocapillaris flow in oligoarticular juvenile idiopathic arthritis (oJIA). Study design: Prospective. Methods: Twenty-two eyes of 22 oJIA patients with uveitis (oJIA-U), 20 eyes of 20 oJIA patients without uveitis (isolated oJIA), and 26 healthy volunteers of similar ages and sexes were investigated. The superficial capillary plexus (SCP) and deep capillary plexus (DCP), ONH, foveal avascular zone (FAZ) parameters, the flow area of the outer retina, and choriocapillaris were evaluated. Results: Compared with the control group, both the oJIA-U group and isolated oJIA group showed significantly decreased vessel density of parafovea (p = 0.031 and p = 0.047, respectively) in DCP. Choriocapillaris flow area at 1 mm radius was significantly lower in the oJIA-U group compared to the control group (p = 0.001). Choriocapillaris flow area at 2- and 3-mm radius were significantly lower in the oJIA-U group compared to the control group (p < 0.001, for both) and isolated oJIA-U group compared to the control group (p = 0.008 and p = 0.001, respectively). The VD and thickness parameters of SCP and ONH, FAZ, and outer retina flow area were similar between the groups. Conclusions: oJIA patients with and without uveitis revealed a decreased vessel density in the deep parafoveal region and choriocapillaris flow. Our findings suggest that retinal choroidal microvascular changes could be evident in oJIA-U patients without posterior segment involvement as well as oJIA patients without uveitis. [ABSTRACT FROM AUTHOR]

Accurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U‐Net structure. The enhanced U‐Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze‐and‐excitation blocks) to adaptively extract image features after each U‐Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U‐Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance. [ABSTRACT FROM AUTHOR]

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet's generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations. [ABSTRACT FROM AUTHOR]

Face sketch synthesis (FSS) is considered an image-to-image translation problem, where a face sketch is generated from an input face photo. FSS plays a vital role in video/image surveillance-based law enforcement. In this paper, motivated by the recent success of generative adversarial networks (GAN), we consider conditional GAN (cGAN) to approach the problem of face sketch synthesis. However, despite the powerful cGAN model ability to generate fine textures, low-quality inputs characterized by the facial sketches drawn by artists cannot offer realistic and faithful details and have unknown degradation due to the drawing process, while high-quality references are inaccessible or even nonexistent. In this context, we propose an approach based on generative reference prior (GRP) to improve the synthesized face sketch perception. Our proposed model, which we call cGAN-GRP, leverages diverse and rich priors encapsulated in a pre-trained face GAN for generating highquality facial sketch synthesis. Extensive experiments on publicly available face databases using facial sketch recognition rate and image quality assessment metrics as criteria demonstrate the effectiveness of our proposed model compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the effects and molecular mechanisms of tetrahedral framework nucleic acids‐microRNA22 (tFNAs‐miR22) on inhibiting pathological retinal neovascularization (RNV) and restoring physiological retinal vessels. A novel DNA nanocomplex (tFNAs‐miR22) was synthesised by modifying microRNA‐22 (miR22) through attachment onto tetrahedral frame nucleic acids (tFNAs), which possess diverse biological functions. Cell proliferation, wound healing, and tube formation were employed for in vitro assays to investigate the angiogenic function of cells. Oxygen‐induced retinopathy (OIR) model was utilised to examine the effects of reducing pathological neovascularization (RNV) and inhibiting vascular occlusion in vivo. In vitro, tFNAs‐miR22 demonstrated the ability to penetrate endothelial cells and effectively suppress cell proliferation, tube formation, and migration in a hypoxic environment. In vivo, tFNAs‐miR22 exhibited promising results in reducing RNV and promoting the restoration of normal retinal blood vessels in OIR model through modulation of the Wnt pathway. This study provided a theoretical basis for the further understanding of RNV, and highlighted the innovative and potential of tFNAs‐miR22 as a therapeutic option for ischemic retinal diseases. [ABSTRACT FROM AUTHOR]

This article presents a study on a new genetic rearrangement that is linked to a condition called PRPF31-associated retinitis pigmentosa (RP). RP is a genetic eye disorder that causes the degeneration of certain cells in the retina, leading to vision problems. The researchers discovered a specific deletion on chromosome 19 that includes the PRPF31 gene, which is associated with RP. They also observed reduced levels of PRPF31 in RP patients. This research offers valuable insights into the genetic causes of RP and could potentially contribute to the development of targeted treatments. Additionally, the study explores a specific mechanism called Alu/Alu-mediated rearrangement (AAMR) that is responsible for this genetic rearrangement. [Extracted from the article]

Patients with cystic fibrosis (CF) are at risk to develop CF related diabetes (CFRD) and subsequently even diabetic neuro- and/or vasculopathy. We sought to determine if there are typical signs of diabetes-related retinal alterations present in CF patients with preserved and impaired glycemic control. During routine annual examination CF patients were offered an additional 7-day period of real time continuous glucose monitoring (rtCGM) and an ophthalmological examination including retinal optical coherence tomography (OCT). Patients were categorized according to the glycemic control, i.e. the results of an oral glucose tolerance test (OGTT) and rtCGM were taken into consideration. OCT data was analyzed by our previously published visual analysis software generating dedicated and spatially resolved deviation maps for visualization and quantification of differences in total retinal thickness and thickness of retinal nerve fiber layer (RNFL) as well as ganglion cell layer (GCL) in comparison to age-matched healthy controls and patients with either type 1 or type 2 diabetes mellitus. Results of the rtCGM and/or OGTT enabled discrimination between patients with normal glycemic control (CFNG; n = 6), with abnormal glycemic control (CFAG; n = 6) and overt CFRD (n = 4). OCT data indicates gradually increasing retinal thinning in all 3 groups, depending on the degree of glucose metabolism disorder compared to healthy controls. At the foveal region total retinal thickness and GCL thickness were significantly thinner in CFRD patients compared to CFNG patients (total retinal thickness: 260.4 µm (239.3–270.8) vs. 275.4 µm (254.3–289.5); GCL: 11.82 µm (11.16–15.25) vs. 17.30 µm (13.95–19.82); each p < 0.05). Although we investigated a rather small number of patients, we obtained evidence that intraretinal neurodegenerative changes occur in each of our subgroups (CFNG, CFAG, CFRD). Beyond this, our results favor the detrimental role of additional diabetes, as the deviations from healthy controls were most pronounced in the CFRD group and are similar to those seen in patients suffering from type 1 or type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: While retinal vessel changes are evident in the eyes of patients with relapsing–remitting multiple sclerosis (RRMS), changes in the vasculature of possible MS mimics such as primary Sjögren's syndrome (pSS) remain to be determined. We investigated the potential of retinal optical coherence tomography (OCT) angiography (OCTA) as diagnostic tool to differentiate between patients with RRMS and pSS. Methods: This cross-sectional study included patients with RRMS (n = 36), pSS (n = 36) and healthy controls (n = 30). Participants underwent clinical examination, assessment of visual acuity, retinal OCT, OCTA, and serum markers of glial and neuronal damage. We investigated the associations between OCTA parameters, visual functions, and serum markers. Eyes with a history of optic neuritis (ON) were excluded from analysis. Results: We observed a significant thinning of the combined ganglion cell and inner plexiform layer in the eyes of patients with RRMS but not with pSS, when compared to healthy controls. Retinal vessel densities of the superficial vascular complex (SVC) were reduced in both patients with RRMS and pSS. However, retinal vessel rarefication of the deep vascular complex (DVC) was only evident in patients with pSS but not RRMS. Using multivariate regression analysis, we found that DVC vessel loss in pSS patients was associated with worse visual acuity. Conclusions: Compared to patients with RRMS, rarefication of deep retinal vessels is a unique characteristic of pSS and associated with worse visual function. Assuming a disease-specific retinal vessel pathology, these data are indicative of a differential affliction of the gliovascular complex in the retina of RRMS and pSS patients. [ABSTRACT FROM AUTHOR]

(1) Background: The fundus examination is one of the best and popular methods in the assessment of vascular status in the human body. Direct viewing of retinal vessels by ophthalmoscopy has been utilized in judging hypertensive change or arteriosclerosis. Recently, fundus imaging with the non-mydriatic scanning laser ophthalmoscope (SLO) has been widely used in ophthalmological clinics since it has multimodal functions for optical coherence tomography or angiography with contrast agent dye. The purpose of this study was to examine the utility in detecting arteriosclerosis of retinal vessels in SLO images; (2) Methods: Both color and blue standard field SLO images of eyes with diabetic retinopathy (DR) were examined retrospectively. Retinal arteriosclerosis in color SLO images was graded according to the Scheie classification. Additionally, characteristics of retinal arterioles in blue SLO images were identified and examined for their relevance to arteriosclerosis grades, stages of DR or general complications; (3) Results: Relative to color fundus images, blue SLO images showed distinct hyper-reflective retinal arterioles against a monotone background. Irregularities of retinal arterioles identified in blue SLO images were frequently observed in the eyes of patients with severe arteriosclerosis (Grade 3: 79.0% and Grade 4: 81.8%). Furthermore, the findings on arterioles were more frequently associated with the eyes of DR patients with renal dysfunction (p < 0.05); (4) Conclusions: While color SLO images are equally as useful in assessing retinal arteriosclerosis as photography or ophthalmoscopy, the corresponding blue SLO images show arteriosclerotic lesions with high contrast in a monotone background. Retinal arteriosclerosis in eyes of advanced grades or advanced DR frequently show irregularities of retinal arterioles in the blue images. The findings of low, uneven, or discontinuous attenuation were easier to find in blue than in color SLO images. Consequently, blue SLO images can show pathological micro-sclerosis in retinal arterioles and are potentially one of the safe and practical methods for the vascular assessment of diabetic patients. [ABSTRACT FROM AUTHOR]

To assess the efficacy of treatment on acute posterior multifocal placoid pigment epitheliopathy (APMPPE) and relentless placoid chorioretinopathy (RPC). Cases were identified from three UK uveitis centers. Retrospective analysis of visual acuity recovery; OCT structural outcomes; and retinal lesion quantification in observed and treated cases of APMPPE/RPC. There were nine APMPPE and three RPC cases. Out of 12 patients, six were female. Median age: 26.5 years (range, 20–57 years). Four cases (six eyes) were observed, and eight cases (15 eyes) received corticosteroids ± immunosuppression. 4/4 observed and 6/10 treated foveal involving eyes regained 0.00 LogMAR vision. Observed lesions achieved more favorable anatomical outcomes. New lesions post-presentation developed in 1/6 (16%) observed eye versus 10/15 (66%) treated eyes. In three cases, a delayed, rebound lesion occurrence was observed post-high-dose corticosteroids. While subject to potential treatment bias, in this small case series, natural history alone appears non-inferior to corticosteroid treatment. [ABSTRACT FROM AUTHOR]