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High-quality imaging of the retina is crucial to the diagnosis and monitoring of disease, as well as for evaluating the success of therapeutics in human patients and in preclinical animal models. Here, we describe the basic principles and methods for in vivo retinal imaging in rodents, including fundus imaging, fluorescein angiography, optical coherence tomography, fundus autofluorescence, and infrared imaging. After providing a concise overview of each method and detailing the retinal diseases and conditions that can be visualized through them, we will proceed to discuss the advantages and disadvantages of each approach. These protocols will facilitate the acquisition of optimal images for subsequent quantification and analysis. Additionally, a brief explanation will be given regarding the potential results and the clinical significance of the detected abnormalities.

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Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.

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The incorporation of gold nanoparticles (GNPs) into retinal imaging signifies a notable advancement in ophthalmology, offering improved accuracy in diagnosis and patient outcomes. This review explores the synthesis and unique properties of GNPs, highlighting their adjustable surface plasmon resonance, biocompatibility, and excellent optical absorption and scattering abilities. These features make GNPs advantageous contrast agents, enhancing the precision and quality of various imaging modalities, including photoacoustic imaging, optical coherence tomography, and fluorescence imaging. This paper analyzes the unique properties and corresponding mechanisms based on the morphological features of GNPs, highlighting the potential of GNPs in retinal disease diagnosis and management. Given the limitations currently encountered in clinical applications of GNPs, the approaches and strategies to overcome these limitations are also discussed. These findings suggest that the properties and efficacy of GNPs have innovative applications in retinal disease imaging.

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Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

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PURPOSE: To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases. METHODS: We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles. RESULTS: Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases. CONCLUSIONS: AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.

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In ophthalmology, Optical Coherence Tomography (OCT) has become a daily used tool in the diagnostics and therapeutic planning of various diseases. Publicly available datasets play a crucial role in advancing research by providing access to diverse imaging data for algorithm development. The accessibility, data format, annotations, and metadata are not consistent across OCT datasets, making it challenging to efficiently use the available resources. This article provides a comprehensive analysis of different OCT datasets, with particular attention to dataset properties, disease representation, accessibility, and aims to create a catalog of all publicly available OCT datasets. The goal is to improve accessibility to OCT data, increase openness about the availability, and give important new perspectives on the state of OCT imaging resources. Our findings reveal the need for improved data-sharing practices and standardized documentation.

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PURPOSE: To determine the importance of optical coherence tomography (OCT) in patients scheduled for cataract surgery who present with no pathologies in biomicroscopic fundus examination. DESIGN: Retrospective study. METHODS: In this study, the routine ophthalmologic examination of patients who were recommended cataract surgery was performed.Occult retinal pathologies were evaluated using OCT in patients without any pathologies in biomicroscopic fundus examination.According to whether retinal pathologies were detected on OCT, the patients were divided into two groups: normal and abnormal OCT.The findings of patients with retinal pathologies on OCT and their distribution according to age were also evaluated. RESULTS: A total of 271 eyes from 271 patients were evaluated.The number of patients with retinal pathologies on OCT despite normal fundoscopic examination findings was 38(14.0%).Of these patients,15(39.4%) had an epiretinal membrane,10(26.3%) had age-related macular degeneration, eight(21%) had vitreomacular traction, two(5.2%) had a lamellar hole, and 1(2.6%) patient each had a full-thickness macular hole, an intraretinal cyst, and photoreceptor layer damage.The age distribution of the patients with retinal pathologies was as follows: two patients,<60 years; six patients,60-70 years;14 patients,70-80 years; and 16 patients,>80 years.The rate of patients aged > 70 years and above was 78.9%.There was no statistically significant difference between the normal and abnormal OCT groups in terms of age, gender, the presence of systemic diseases, visual acuity, central macular thickness, and cataract type or density(p > 0.05 for all). CONCLUSION: In one of seven patients evaluated, retinal pathologies were detected on OCT despite normal fundoscopic examination findings.OCT can be used to detect occult retinal pathologies that cannot be detected by biomicroscopic fundus examination before cataract surgery.

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INTRODUCTION: We aimed to analyze the thicknesses of the retinal sublayer and peripapillary retinal nerve fiber layer (pRNFL) in patients with juvenile systemic lupus erythematosus (JSLE) without lupus retinopathy. METHODS: Thirty-six patients with JSLE (36 eyes) and 30 control subjects (30 eyes) were included retrospectively. Demographic data, disease duration, and clinical manifestations were recorded. Optical coherence tomography was used to examine the macula and optic disc. The thicknesses of the retina, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), retinal pigment epithelium (RPE), and pRNFL were measured. The correlation between the thickness of retina and disease duration, erythrocyte sedimentation rate (ESR) were investigated. RESULTS: The retinal thicknesses of I3 and T3 were thinner in the JSLE group than in the control group (P = 0.019, P = 0.043, respectively). The thicknesses of the I3 and S6 sectors of the GCL decreased significantly (P = 0.013, and P = 0.022, respectively). The thickness of the S6 sector of the IPL was reduced in the JSLE group compared with the control group (P = 0.047). The JSLE group showed significant decrease in the thickness of the central sector of the ONL (P = 0.034). No statistically significant differences in INL, OPL, RPE, and pRNFL thicknesses were found. The retinal thicknesses of I3 (r = -0.386, P = 0.020) and T3 (r = -0.384, P = 0.021) presented negative associations with ESR, but had no significant correlations with disease duration. CONCLUSIONS: Retinal thinning was observed in patients with JSLE without lupus retinopathy, and this change was more pronounced in the inner layer. Key Points • Retinal thinning occurs in JSLE patients without lupus retinopathy. • Changes in retinal thicknesses are related to the ESR.

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RATIONALE: Autosomal recessive bestrophinopathy (ARB) is a subtype of bestrophinopathy caused by biallelic mutations of the BEST1 gene, which affect the retinal pigment epithelium (RPE). Studying RPE abnormalities through imaging is essential for understanding ARB. This case series involved the use of multimodal imaging techniques, namely autofluorescence (AF) imaging at 488 nm [short-wavelength AF] and 785 nm [near-infrared AF (NIR-AF)] and polarization-sensitive optical coherence tomography (PS-OCT), to investigate RPE changes in 2 siblings with ARB. PATIENT CONCERNS: Two Japanese siblings (Case 1: male, followed for 20-23 years; Case 2: female, followed for 13-17 years) carried compound heterozygous mutations of the BEST1 gene. DIAGNOSIS: Both siblings were diagnosed with ARB. INTERVENTIONS AND OUTCOMES: Multimodal imaging techniques were used to evaluate RPE changes. Both siblings had funduscopic changes similar to those seen in the vitelliruptive stage of Best vitelliform macular dystrophy during the follow-up period. NIR-AF imaging showed hypo-AF of the entire macular lesion in both cases, and this hypo-AF remained stable over time. PS-OCT confirmed reduced RPE melanin content in these hypo-AF areas. Additionally, hyper-NIR-AF dots were observed within hypo-NIR-AF areas. Concomitant identification of focally thickened RPE melanin on PS-OCT imaging and hyper-AF on short-wavelength AF imaging at the sites containing hyper-NIR-AF dots indicated that the hyper-NIR-AF dots had originated from either stacked RPE cells or RPE dysmorphia. LESSONS: We confirmed RPE abnormalities in ARB, including diffuse RPE melanin damage in the macula alongside evidence of RPE activity-related changes. This case series demonstrates that multimodal imaging, particularly NIR-AF and PS-OCT, provides detailed insights into RPE alterations in ARB.

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Fundus imaging plays a pivotal role in diagnosing retinal and choroidal diseases. Optical coherence tomography angiography (OCTA), by capturing signals to reconstruct vascular structures, offers a clear depiction of retinal vasculature with notable advantages such as rapid scanning and non-invasiveness. Although OCTA, due to its underlying principles, cannot dynamically assess vascular function, exploring its future applications and potential to eventually replace traditional fundus angiography remains a key focus in the medical community. OCTA provides multiple parameters that conventional fundus angiography cannot obtain. With the expanding coverage area of OCTA scans and improvements in artifact elimination, the detection rate of various retinal and choroidal diseases has significantly increased, making the widespread clinical application of OCTA an inevitable trend. Although ultra-widefield OCTA cannot yet fully replace angiography in clinical practice, with continued clinical practice, expanded clinical research, and ongoing technological innovation, OCTA is expected to gradually replace fundus angiography in the future.

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This review is devoted to the English- and Russian-language terminology of quantitative metrics that are used in the evaluation of images obtained by optical coherence tomography angiography (OCT-A). The paper presents an analysis of the use of terms characterizing intraretinal blood flow (vascular density, perfusion density, skeletonized density, etc.), area and shape of the foveal avascular zone, and choriocapillaris blood flow. The factors causing the heterogeneity of OCT-A terminology are described, including the lack of a unified international nomenclature for OCT-A, features of their Russian translation, inconsistency of the parameters in optical coherence tomography systems of different manufacturers. The article also considers ways to standardize the terminology.

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The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.

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Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

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BACKGROUND AND OBJECTIVE: This paper proposes a fully automated and unsupervised stochastic segmentation approach using two-level joint Markov-Gibbs Random Field (MGRF) to detect the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, which is a critical step in developing Computer-Aided Diagnosis (CAD) systems for detecting retinal diseases. METHODS: Using a new probabilistic model based on a Linear Combination of Discrete Gaussian (LCDG), the first level models the appearance of OCTA images and their spatially smoothed images. The parameters of the LCDG model are estimated using a modified Expectation Maximization (EM) algorithm. The second level models the maps of OCTA images, including the vascular system and other retina tissues, using MGRF with analytically estimated parameters from the input images. The proposed segmentation approach employs modified self-organizing maps as a MAP-based optimizer maximizing the joint likelihood and handles the Joint MGRF model in a new, unsupervised way. This approach deviates from traditional stochastic optimization approaches and leverages non-linear optimization to achieve more accurate segmentation results. RESULTS: The proposed segmentation framework is evaluated quantitatively on a dataset of 204 subjects. Achieving 0.92 ± 0.03 Dice similarity coefficient, 0.69 ± 0.25 95-percentile bidirectional Hausdorff distance, and 0.93 ± 0.03 accuracy, confirms the superior performance of the proposed approach. CONCLUSIONS: The conclusions drawn from the study highlight the superior performance of the proposed unsupervised and fully automated segmentation approach in detecting the vascular system from OCTA images. This approach not only deviates from traditional methods but also achieves more accurate segmentation results, demonstrating its potential in aiding the development of CAD systems for detecting retinal diseases.

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The relationship between amyloidosis and vasculature in cognitive impairment and Alzheimer's disease (AD) pathogenesis is increasingly acknowledged. We conducted a quantitative and topographic assessment of retinal perivascular amyloid plaque (AP) distribution in individuals with both normal and impaired cognition. Using a retrospective dataset of scanning laser ophthalmoscopy fluorescence images from twenty-eight subjects with varying cognitive states, we developed a novel image processing method to examine retinal peri-arteriolar and peri-venular curcumin-positive AP burden. We further correlated retinal perivascular amyloidosis with neuroimaging measures and neurocognitive scores. Our study unveiled that peri-arteriolar AP counts surpassed peri-venular counts throughout the entire cohort (P < 0.0001), irrespective of the primary, secondary, or tertiary vascular branch location, with a notable increase among cognitively impaired individuals. Moreover, secondary branch peri-venular AP count was elevated in the cognitively impaired (P < 0.01). Significantly, peri-venular AP count, particularly in secondary and tertiary venules, exhibited a strong correlation with clinical dementia rating, Montreal cognitive assessment score, hippocampal volume, and white matter hyperintensity count. In conclusion, our exploratory analysis detected greater peri-arteriolar versus peri-venular amyloidosis and a marked elevation of amyloid deposition in secondary branch peri-venular regions among cognitively impaired subjects. These findings underscore the potential feasibility of retinal perivascular amyloid imaging in predicting cognitive decline and AD progression. Larger longitudinal studies encompassing diverse populations and AD-biomarker confirmation are warranted to delineate the temporal-spatial dynamics of retinal perivascular amyloid deposition in cognitive impairment and the AD continuum.

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To define the characteristics of fundus manifestations in patients after SARS-CoV-2 infection with multimodal imaging techniques. This is a retrospective multicenter and multimodal imaging study including 90 patients. All patients with a visual complaint occurring immediately after SARS-CoV-2 infection were referred to six clinics between December 2022 and February 2023. Demographic information and the temporal relationship between SARS-CoV-2 infection and visual symptoms were documented. The characteristics of the fundus lesions were evaluated using multimodal imaging. Ninety patients from six hospitals were included in this study, including 24 males (26.67%) and 66 (73.33%) females. Seventy-eight patients (86.66%) (146 eyes) were diagnosed with Acute Macular Neuroretinopathy (AMN). The AMN patients were primarily young women (67.95%). Sixty-eight patients (87.18%) had AMN in both eyes. Thirty-eight eyes (24.36%) included Purtscher or Purtscher-like lesions. optical coherence tomography and infrared retinal photographs can show AMN lesions well. Eleven cases were diagnosed with simple Purtscher or Purtscher-like retinopathy (2 cases, 2.22%), Vogt‒Koyanagi‒Harada (VKH) syndrome or VKH-like uveitis (3 cases, 3.33%), multiple evanescent white-dot syndrome (MEWDS) (2 cases, 2.22%), and rhino-orbital-cerebral mucormycosis (ROCM) (5 cases, 5.56%). After SARS-CoV-2 infection, diversified fundus lesions were evident in patients with visual complaints. In this report, AMN was the dominant manifestation, followed by Purtscher or Purtscher-like retinopathy, MEWDS, VKH-like uveitis, and ROCM.

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The aim of the study was to characterize retinal atrophy (RA) with progressive retinal atrophy symptoms in mixed breed dogs using ophthalmoscopy, spectral domain optical coherence tomography (SD-OCT) and electroretinography (ERG).The study was performed on 13 mixed breed dogs affected by retinal atrophy (11 males and 2 females that were 1.5-14 years old). Depending on the advancement of RA, SD-OCT examinations identified retinal abnormalities ranging from layer disorganisation to advanced atrophy. The most advanced RA occurred ventral to the optic disc. Total retinal thickness in both eyes (mean ± SD) was lower in dogs with RA compared to controls dorsally (77.7 ± 39.5 µm vs 173.5 ± 13.3 µm), ventrally (33.4 ± 29.9 µm vs 139.5 ± 10.8 µm), nasally (65.0 ± 34.5 µm vs 163.9 ± 11.0 µm) and temporally (61.8 ± 41.7 µm vs 171.9 ± 11.1 µm) to the optic disc. In dogs with locally normal architecture of inner retina, loss of definition of outer retinal layers occurred in many regions. Dark and light-adapted ERGs were reduced in 2 dogs with RA and were unrecordable in 11 dogs. Lesions evident in SD-OCT scans of mixed breed dogs affected with retinal atrophy initially appear ventrally to the optic disc and ventro-dorsally in advanced RA. In all mixed breed dogs with retinal atrophy, clinical signs and SD-OCT results correlate with ERG findings.

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Poppers maculopathy is a complication of alkyl nitrate (poppers) inhalation. It presents with non-specific symptoms and variable signs, which can make it difficult to diagnose. We present a case of coexisting cataract and poppers maculopathy in a patient. He had vague visual symptoms that were attributed entirely to his cataract and he went on to have cataract surgery. Suboptimal postoperative visual acuity and normal clinical examination triggered further investigation with spectral-domain optical coherence tomography (SD-OCT), after which poppers maculopathy was diagnosed. We highlight the importance of performing OCT in the preoperative assessment of a cataract patient, especially where the cataract is mild and may not fully account for symptoms. The patient showed complete visual recovery on drug cessation despite ongoing maculopathy on OCT scans.

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