Search Results (78)

Diabetic retinopathy (DR), a leading cause of blindness in working-age adults, arises from chronic hyperglycemia-induced oxidative stress, inflammation, and vascular dysfunction. Current therapies such as laser photocoagulation, intravitreal anti-vascular endothelial growth factor (VEGF) agents, and steroids target advanced stages but fail to prevent early neuronal and microvascular damage. Emerging evidence highlights oxidative stress as a key driver of DR pathogenesis, disrupting the blood-retinal barrier (BRB), promoting neurodegeneration and angiogenesis. Advances in imaging, particularly optical coherence tomography angiography (OCTA), enable earlier detection of neurodegeneration and microvascular changes, underscoring DR as a neurovascular disorder. Polyphenols, such as resveratrol, curcumin, and pterostilbene, exhibit multitarget antioxidant, anti-inflammatory, and anti-angiogenic effects, showing promise in preclinical and limited clinical studies. However, their low bioavailability limits therapeutic efficacy. Nanotechnology-based delivery systems enhance drug stability, tissue targeting, and sustained release, offering potential for early intervention. Future strategies should integrate antioxidant therapies and precision diagnostics to prevent early irreversible retinal damage in diabetic patients. Full article

Background/Objectives: The aim of this study was to evaluate the associations between optical coherence tomography angiography (OCTA)-based retinal vessel diameter (RVD) measurements, with retinal ganglion cell (RGC) function assessed by means of steady-state pattern electroretinography (ssPERG) using ganglion cell layer-inner plexiform layer thickness (GCL-IPLT) measurements and with Humphrey field analyzer (HFA) global indices in glaucoma suspects (GSs). Methods: Thirty-one eyes (20 participants) underwent a comprehensive ophthalmologic examination, ssPERG measurements utilizing the PERGLA paradigm, HFA, optical coherence tomography (OCT), and OCTA. The OCTA scans were processed using ImageJ software, Version 1.53, allowing for measurement of the RVD. Multiple linear regression models were used. Results: After controlling for age, race, central corneal thickness (CCT), and spherical equivalent (SE), a linear regression analysis found that the RVD explained the 4.7% variance in magnitude (Mag) (p = 0.169), 9.2% variance in magnitudeD (MagD) (p = 0.021), and 16.9% variance in magnitudeD/magnitude (p = 0.009). After controlling for age, CCT, and signal strength (SS), a linear regression analysis found that the RVD was significantly associated with the GCL-IPLT measurements (average GCL-IPL, minimum GCL-IPL, superior, superonasal, inferior, and inferonasal sectors) (p ≤ 0.023). An identical regression analysis where the RVD was replaced with the PERG parameters showed a significant association between the MagD and almost all GCI-IPLT measurements. RVD measurements were significantly associated with HFA VFI 24-2 (p = 0.004), MD 24-2 (p < 0.001), and PSD 24-2 (p = 0.009). Conclusions: Decreased RVD measurements were significantly associated with RGC dysfunction, decreased GCL-IPLT, and all HFA global indices in the GSs. Full article

Background: Cardiovascular diseases (CVDs) are responsible for 32.4% of all deaths across the European Union (EU), and several CVD risk scores have been developed, with variable results. Retinal microvascular changes have been proposed as potential biomarkers for cardiovascular risk, especially in coronary heart diseases (CHDs). This study aims to identify the retinal microvascular features associated with CHDs and evaluate their potential use in a CHD screening algorithm in conjunction with traditional risk factors. Methods: We performed a two-center cross-sectional study on 120 adult participants—36 patients previously diagnosed with severe CHDs and scheduled for coronary artery bypass graft surgery (CHD group) and 84 healthy controls. A brief medical history and a clinical profile were available for all cases. All patients benefited from optical coherence tomography angiography (OCTA), the use of which allowed several parameters to be quantified for the foveal avascular zone and superficial and deep capillary plexuses. We evaluated the precision of several classification models in identifying patients with CHDs based on traditional risk factors and OCTA characteristics: a conventional logistic regression model and four machine learning algorithms: k-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM) and supervised logistic regression. Results: Conventional multiple logistic regression had a classification accuracy of 78.7% based on traditional risk factors and retinal microvascular features, while machine learning algorithms had higher accuracies: 81% for K-NN and supervised logistic regression, 85.71% for Naive Bayes and 86% for SVM. Conclusions: Novel risk scores developed using machine learning algorithms and based on traditional risk factors and retinal microvascular characteristics could improve the identification of patients with CHDs. Full article

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique used to visualize retinal blood flow and identify changes in vascular density and enlargement or distortion of the foveal avascular zone (FAZ), which are indicators of various eye diseases. Although several automated FAZ detection and segmentation algorithms have been developed for use with OCTA, their performance can vary significantly due to differences in data accessibility of OCTA in different retinal pathologies, and differences in image quality in different subjects and/or different OCTA devices. For example, data from subjects with direct macular damage, such as in age-related macular degeneration (AMD), are more readily available in eye clinics, while data on macular damage due to systemic diseases like Alzheimer’s disease are often less accessible; data from healthy subjects may have better OCTA quality than subjects with ophthalmic pathologies. Typically, segmentation algorithms make use of convolutional neural networks and, more recently, vision transformers, which make use of both long-range context and fine-grained detail. However, transformers are known to be data-hungry, and may overfit small datasets, such as those common for FAZ segmentation in OCTA, to which there is limited access in clinical practice. To improve model generalization in low-data or imbalanced settings, we propose a multi-condition transformer-based architecture that uses four teacher encoders to distill knowledge into a shared base model, enabling the transfer of learned features across multiple datasets. These include intra-modality distillation using OCTA datasets from four ocular conditions: healthy aging eyes, Alzheimer’s disease, AMD, and diabetic retinopathy; and inter-modality distillation incorporating color fundus photographs of subjects undergoing laser photocoagulation therapy. Our multi-condition model achieved a mean Dice Index of 83.8% with pretraining, outperforming single-condition models (mean of 83.1%) across all conditions. Pretraining on color fundus photocoagulation images improved the average Dice Index by a small margin on all conditions except AMD (1.1% on single-condition models, and 0.1% on multi-condition models). Our architecture demonstrates potential for broader applications in detecting and analyzing ophthalmic and systemic diseases across diverse imaging datasets and settings. Full article

Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools. Full article

Background/Objectives: Considering the physiological analogies between the eye and the kidney, this study aimed to investigate the potential relationship between retinal vascular density, assessed using Optical Coherence Tomography Angiography (OCT-A), and the renal resistive index (RRI) in patients with arterial hypertension. Methods: A total of 82 hypertensive patients (mean age 48 ± 13) were enrolled in the study. Participants underwent routine biochemical evaluations, office-based blood pressure measurement, 24 h ambulatory blood pressure monitoring, OCT-A imaging, and renal Doppler ultrasound examinations. Results: The mean RRI in the study population was 0.616 ± 0.06. Participants were divided into two groups based on the 75th percentile threshold of the RRI distribution (0.66, 95% CI 0.64–0.68). The group with RRI > 75th percentile, which appeared to have a higher number of smokers, exhibited significantly higher mean triglyceride and urinary albumin excretion (UAE) levels and a significantly reduced estimated glomerular filtration rate (eGFR) as compared to the group with RRI < 75th percentile. Among the hemodynamic parameters, 24 h pulse pressure (PP), daytime and nighttime PP, and nighttime systolic blood pressure (SBP) were significantly higher in the group with RRI > 75th percentile. Regarding retinal vascular density indices, the only significant difference was observed in the deep foveal vascular plexus, which displayed a reduced density in the group with RRI > 75th percentile. Logistic regression analysis revealed that RRI > 75th percentile was independently associated with increased nighttime mean pulse pressure (OR = 1.13, 95% CI: 1.049–1.221, p = 0.0014) and reduced deep foveal vascular density (OR = −0.5026, 95% CI: 1.0493–1.2211, p = 0.0044). Conclusions: Our findings demonstrate that ocular microvascular alterations are associated with RRI, a marker with a well-established prognostic value for renal disease progression and systemic macrovascular dysfunction. These results further substantiate the close relationship between renal and ocular microcirculation. Full article

Background: This study aims to evaluate the quantitative changes in retinal nerve fiber layer (RNFL) thickness and radial peripapillary capillary (RPC) vessel density in patients with chronic central serous chorioretinopathy (CSCR), specifically excluding the peripapillary region. Methods: A prospective case–control study was conducted at the Antalya Training and Research Hospital, Health Sciences University, involving 65 patients with chronic CSCR. Participants were categorized into two groups based on the presence or regression of subretinal fluid (SRF). A control group of age- and sex-matched healthy individuals was also included. Optical coherence tomography angiography (OCTA) was used to assess RNFL thickness and RPC vessel density. Statistical analyses were conducted using SPSS, with non-parametric tests employed for between-group comparisons. Results: Patients with persistent SRF exhibited significant increases in RNFL thickness in the inferior and nasal quadrants compared to healthy controls (p = 0.003 and p = 0.014, respectively). Additionally, RPC vessel density in the small vessel disc area (%) was significantly lower in the persistent SRF group compared to controls (p = 0.021). A significant negative correlation was found between nasal quadrant RNFL thickness and small vessel disc area (p = 0.014, r = −0.306). Conclusions: Chronic SRF in CSCR patients, even when not involving the peripapillary region, leads to significant structural changes in both the neural and vascular components of the retina. These findings suggest that SRF contributes to broader retinal alterations and supports the need for early detection and management of CSCR to prevent long-term visual impairment. Full article

Dye-based angiography is the main imaging modality in evaluating the vasculature of the eye. Although most commonly used to assess retinal vasculature, it can also delineate normal and abnormal blood vessels in the anterior segment diseases—but is limited due to its invasive, time-consuming methods. Thus, anterior segment optical coherence tomography angiography (AS-OCTA) is a useful non-invasive modality capable of producing high-resolution images to evaluate the cornea and ocular surface vasculature. AS-OCTA has demonstrated the potential to detect and delineate blood vessels in the anterior segment with quality images comparable to dye-based angiography. AS-OCTA has a diverse range of applications for the cornea and ocular surface, such as objective assessment of corneal neovascularization and response to various treatments; diagnosis and evaluation of ocular surface squamous neoplasia; and evaluation of ocular surface disease including limbal stem cell deficiency and ischemia. Our review aims to summarize the new developments and clinical applications of AS-OCTA for the cornea and ocular surface. Full article

Optical Coherence Tomography (OCT)-based angiography (OCTA) is a high-resolution, high-speed, and non-invasive imaging method that can provide vascular mapping of subcutaneous tissue up to approximately 2 mm. In dermatology applications of OCTA, handheld probes are always designed with a piece of transparent but solid contact window placed at the end of the probe to directly contact the skin for achieving better focusing between the light source and the tissue, reducing noise caused by minor movements. The pressure between the contact window and the skin is usually uncontrollable, and high external pressure affects the quality of microvascular imaging by compressing the vessels and obstructing the underlying blood flow. Therefore, it is necessary to determine a pressure range to ensure that the vessels can be fully imaged in high-quality images. In this paper, two pressure sensors were added to the existing handheld OCT probe, and the imaging probe was fixed to a metal stand and adjusted vertically to change the pressure between the probe and the tested skin site, a gradient of roughly 4 kPa (with 1–2 kPa error) increase was applied in each experiment, and the impact of pressure to the vessel was calculated. The experiment involved a total of five subjects, three areas of which were scanned (palm, back of the hand, and forearm). The vessel density was calculated to evaluate the impact of external pressure on angiography. In addition, PSNR was calculated to ensure that the quality of different tests was at a similar level. The angiography showed the highest density (about 10%) when the pressure between the contact window on the probe and the test area was between 3 and 5 kPa. As the pressure increased, the vascular density decreased, and the rate of decrease varied in different test areas. After fitting all the data points according to the different sites, the slope of the fitted line, i.e., the rate of decrease in density per unit value of pressure, was found to be 4.05% at the palm site, 6.93% at the back of the hand, and 4.55% at the forearm site. This experiment demonstrates that the pressure between the skin and contact window is a significant parameter that cannot be ignored. It is recommended that in future OCTA data collection processes and probe designs, the impact of pressure on the experiment be considered. Full article

The aim of this review is to present and discuss the use of optical coherence tomography angiography (OCTA) in age-related macular degeneration (AMD). OCTA is a non-invasive imaging procedure that gives a detailed indirect view of physiological and pathological vessels in the retina and choroid membrane. Compared with dye-based imaging, OCTA provides a segmented presentation of the individual vascular layers and plexuses, thus enabling previously unattainable differentiation and classification of pathological vascular changes within or underneath the retina. In particular, OCTA facilitates early detection of exudative macular neovascularizations (MNV) so that treatment with anti-VEGF medication can be initiated. Moreover, in the context of both screening and therapy monitoring, it is hoped that OCTA can provide more detailed data to enable greater personalization of treatment and follow-up. The image quality of OCTA is, however, susceptible to artifacts, and validation of the results by studies is required. Recent developments have shown constant improvement both in the algorithms for image calculation and avoidance of artifacts and in image quality, so the scope of OCTA will certainly expand with time. Full article

Background/Objectives: To investigate macular vascular biomarkers for the detection of primary open-angle glaucoma (POAG). Methods: A total of 56 POAG patients and 94 non-glaucomatous controls underwent optical coherence tomography angiography (OCTA) assessment of macular vessel density (VD) in the superficial (SCP), and deep (DCP) capillary plexus, foveal avascular zone (FAZ) area, perimeter, VD, choriocapillaris and outer retina flow area. POAG patients were classified for severity based on the Glaucoma Staging System 2 of Brusini. ANCOVA comparisons adjusted for age, sex, race, hypertension, diabetes, and areas under the receiver operating characteristic curves (AUCs) for POAG/control differentiation were compared using the DeLong method. Results: Global, hemispheric, and quadrant SCP VD was significantly lower in POAG patients in the whole image, parafovea, and perifovea (p < 0.001). No significant differences were found between POAG and controls for DCP VD, FAZ parameters, and the retinal and choriocapillaris flow area (p > 0.05). SCP VD in the whole image and perifovea were significantly lower in POAG patients in stage 2 than stage 0 (p < 0.001). The AUCs of SCP VD in the whole image (0.86) and perifovea (0.84) were significantly higher than the AUCs of all DCP VD (p < 0.05), FAZ parameters (p < 0.001), and retinal (p < 0.001) and choriocapillaris flow areas (p < 0.05). Whole image SCP VD was similar to the AUC of the global retinal nerve fiber layer (RNFL) (AUC = 0.89, p = 0.53) and ganglion cell complex (GCC) thickness (AUC = 0.83, p = 0.42). Conclusions: SCP VD is lower with increasing functional damage in POAG patients. The AUC for SCP VD was similar to RNFL and GCC using clinical diagnosis as the reference standard. Full article

The current methods to generate projections for structural and angiography imaging of Fourier-Domain optical coherence tomography (FD-OCT) are significantly slow for prediagnosis improvement, prognosis, real-time surgery guidance, treatments, and lesion boundary definition. This study introduced a robust ultrafast projection pipeline (RUPP) and aimed to develop and evaluate the efficacy of RUPP. RUPP processes raw interference signals to generate structural projections without the need for Fourier Transform. Various angiography reconstruction algorithms were utilized for efficient projections. Traditional methods were compared to RUPP using PSNR, SSIM, and processing time as evaluation metrics. The study used 22 datasets (hand skin: 9; labial mucosa: 13) from 8 volunteers, acquired with a swept-source optical coherence tomography system. RUPP significantly outperformed traditional methods in processing time, requiring only 0.040 s for structural projections, which is 27 times faster than traditional summation projections. For angiography projections, the best RUPP variation took 0.15 s, making it 7518 times faster than the windowed eigen decomposition method. However, PSNR decreased by 41–45% and SSIM saw reductions of 25–74%. RUPP demonstrated remarkable speed improvements over traditional methods, indicating its potential for real-time structural and angiography projections in FD-OCT, thereby enhancing clinical prediagnosis, prognosis, surgery guidance, and treatment efficacy. Full article

Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of $0.92$ for the DRAC and an AUROC of $0.75$ for the OCTA-500) and localization (a mean Dice score of $0.61$ for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA. Full article

Diabetic retinopathy (DR), a vision-threatening microvascular complication of diabetes mellitus (DM), is a leading cause of blindness worldwide that requires early detection and intervention. However, diagnosing DR early remains challenging due to the subtle nature of initial pathological changes. This review explores developments in multimodal imaging and functional tests for early DR detection. Where conventional color fundus photography is limited in the field of view and resolution, advanced quantitative analysis of retinal vessel traits such as retinal microvascular caliber, tortuosity, and fractal dimension (FD) can provide additional prognostic value. Optical coherence tomography (OCT) has also emerged as a reliable structural imaging tool for assessing retinal and choroidal neurodegenerative changes, which show potential as early DR biomarkers. Optical coherence tomography angiography (OCTA) enables the evaluation of vascular perfusion and the contours of the foveal avascular zone (FAZ), providing valuable insights into early retinal and choroidal vascular changes. Functional tests, including multifocal electroretinography (mfERG), visual evoked potential (VEP), multifocal pupillographic objective perimetry (mfPOP), microperimetry, and contrast sensitivity (CS), offer complementary data on early functional deficits in DR. More importantly, combining structural and functional imaging data may facilitate earlier detection of DR and targeted management strategies based on disease progression. Artificial intelligence (AI) techniques show promise for automated lesion detection, risk stratification, and biomarker discovery from various imaging data. Additionally, hematological parameters, such as neutrophil–lymphocyte ratio (NLR) and neutrophil extracellular traps (NETs), may be useful in predicting DR risk and progression. Although current methods can detect early DR, there is still a need for further research and development of reliable, cost-effective methods for large-scale screening and monitoring of individuals with DM. Full article

This review aims to explore advancements in perioperative ischemic stroke risk estimation for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis (CoW) morphology based on the CTA or MR diagnostic imaging in the current preoperative diagnostic algorithm. Functional transcranial Doppler (fTCD), near-infrared spectroscopy (NIRS), and optical coherence tomography angiography (OCTA) are discussed in the context of evaluating cerebrovascular reserve capacity and collateral vascular systems, particularly the CoW. These non-invasive diagnostic tools provide additional valuable insights into the cerebral perfusion status. They support biomedical modeling as the gold standard for the prediction of the potential impact of carotid artery stenosis on the hemodynamic changes of cerebral perfusion. Intraoperative risk assessment strategies, including selective shunting, are explored with a focus on CoW variations and their implications for perioperative ischemic stroke and cognitive function decline. By synthesizing these insights, this review underscores the potential of non-invasive diagnostic methods to support clinical decision making and improve asymptomatic patient outcomes by reducing the risk of perioperative ischemic neurological events and preventing further cognitive decline. Full article