OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. RESULTS: The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson's R = 0.44, p < 0.001). CONCLUSION: The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model's robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

Vascular changes of the choroid and their correlations with visual acuity in diabetic retinopathy.

Objective: To investigate changes in the choroidal vasculature and their correlations with visual acuity in diabetic retinopathy (DR). Methods: The cohort was composed of 225 eyes from 225 subjects, including 60 eyes from 60 subjects with healthy control, 55 eyes from 55 subjects without DR, 46 eyes from 46 subjects with nonproliferative diabetic retinopathy (NPDR), 21 eyes from 21 subjects with proliferative diabetic retinopathy (PDR), and 43 eyes from 43 subjects with clinically significant macular edema (CSME). Swept-source optical coherence tomography (SS-OCT) was used to image the eyes with a 12-mm radial line scan protocol. The parameters for 6-mm diameters of region centered on the macular fovea were analyzed. Initially, a custom deep learning algorithm based on a modified residual U-Net architecture was utilized for choroidal boundary segmentation. Subsequently, the SS-OCT image was binarized and the Niblack-based automatic local threshold algorithm was employed to calibrate subfoveal choroidal thickness (SFCT), luminal area (LA), and stromal area (SA) by determining the distance between the two boundaries. Finally, the ratio of LA and total choroidal area (SA + LA) was defined as the choroidal vascularity index (CVI). The choroidal parameters in five groups were compared, and correlations of the choroidal parameters with age, gender, duration of diabetes mellitus (DM), glycated hemoglobin (HbA1c), fasting blood sugar, SFCT and best-corrected visual acuity (BCVA) were analyzed. Results: The CVI, SFCT, LA, and SA values of patients with DR were found to be significantly lower compared to both healthy patients and patients without DR (P < 0.05). The SFCT was significantly higher in NPDR group compared to the No DR group (P < 0.001). Additionally, the SFCT was lower in the PDR group compared to the NPDR group (P = 0.014). Furthermore, there was a gradual decrease in CVI with progression of diabetic retinopathy, reaching its lowest value in the PDR group. However, the CVI of the CSME group exhibited a marginally closer proximity to that of the NPDR group. The multivariate regression analysis revealed a positive correlation between CVI and the duration of DM as well as LA (P < 0.05). The results of both univariate and multivariate regression analyses demonstrated a significant positive correlation between CVI and BCVA (P = 0.003). Conclusion: Choroidal vascular alterations, especially decreased CVI, occurred in patients with DR. The CVI decreased with duration of DM and was correlated with visual impairment, indicating that the CVI might be a reliable imaging biomarker to monitor the progression of DR.

Explainable ensemble learning method for OCT detection with transfer learning.

The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection.

Difference of central foveal thickness measurement in patients with macular edema using optical coherence tomography in different display modes.

Purpose: To assess the differences in the measurement of central foveal thickness (CFT) in patients with macular edema (ME) between two display modes (1:1 pixel and 1:1 micron) on optical coherence tomography (OCT). Design: This is a retrospective, cross-sectional study. Methods: Group A consisted of participants with well-horizontal OCT B-scan images and group B consisted of participants with tilted OCT B-scan. We manually measured the CFT under the two display modes, and the values were compared statistically using the paired t-test. Spearman's test was used to assess the correlations between the OCT image tilting angle (OCT ITA) and the differences in CFT measurement. The area under the curve (AUC) was calculated to define the OCT ITA cutoff for a defined CFT difference. Results: In group A, the mean CFT in the 1:1 pixel display mode was 420.21 ± 130.61 µm, similar to the mean CFT of 415.27 ± 129.85 µm in the 1:1 micron display mode. In group B, the median CFT in the 1:1 pixel display mode is 409.00 µm (IQR: 171.75 µm) and 368.00 µm (IQR: 149.00 µm) in the 1:1 micron display mode. There were significant differences between the two display modes with the median (IQR) absolute difference and median (IQR) relative difference of 38.00 µm (75.00 µm) and 10.19% (21.91%) (all p = 0.01). The differences in CFT measurement between the two display modes were correlated with the OCT ITA (absolute differences, r = 0.88, p < 0.01; relative differences, r = 0.87, p < 0.01). The AUC for a predefined CFT difference was 0.878 (10 µm), 0.933 (20 µm), 0.938 (30 µm), 0.961 (40 µm), 0.962 (50 µm), and 0.970 (60 µm). Conclusion: In patients with DM, when the OCT B-scan images were well-horizontal, manual CFT measurements under the two display modes were similar, but when the B-scan images were tilted, the CFT measurements were different under the two display modes, and the differences were correlated to the OCT ITA.

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images.

Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.

BACKGROUND: Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. PURPOSE: To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. DATA SOURCES: We searched seven electronic libraries up to 12 February 2023. STUDY SELECTION: We included studies using AI to detect DME from FP or OCT images. DATA EXTRACTION: We extracted study characteristics and performance parameters. DATA SYNTHESIS: Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation. LIMITATIONS: Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation. CONCLUSIONS: This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.

Improved predictive diagnosis of diabetic macular edema based on hybrid models: An observational study.

Diabetic Macular Edema (DME) is the most common sight-threatening complication of type 2 diabetes. Optical Coherence Tomography (OCT) is the most useful imaging technique to diagnose, follow up, and evaluate treatments for DME. However, OCT exam and devices are expensive and unavailable in all clinics in low- and middle-income countries. Our primary goal was therefore to develop an alternative method to OCT for DME diagnosis by introducing spectral information derived from spontaneous electroretinogram (ERG) signals as a single input or combined with fundus that is much more widespread. Baseline ERGs were recorded in 233 patients and transformed into scalograms and spectrograms via Wavelet and Fourier transforms, respectively. Using transfer learning, distinct Convolutional Neural Networks (CNN) were trained as classifiers for DME using OCT, scalogram, spectrogram, and eye fundus images. Input data were randomly split into training and test sets with a proportion of 80 %-20 %, respectively. The top performers for each input type were selected, OpticNet-71 for OCT, DenseNet-201 for eye fundus, and non-evoked ERG-derived scalograms, to generate a combined model by assigning different weights for each of the selected models. Model validation was performed using a dataset alien to the training phase of the models. None of the models powered by mock ERG-derived input performed well. In contrast, hybrid models showed better results, in particular, the model powered by eye fundus combined with mock ERG-derived information with a 91 % AUC and 86 % F1-score, and the model powered by OCT and mock ERG-derived scalogram images with a 93 % AUC and 89 % F1-score. These data show that the spontaneous ERG-derived input adds predictive value to the fundus- and OCT-based models to diagnose DME, except for the sensitivity of the OCT model which remains the same. The inclusion of mock ERG signals, which have recently been shown to take only 5 min to record in daylight conditions, therefore represents a potential improvement over existing OCT-based models, as well as a reliable and cost-effective alternative when combined with the fundus, especially in underserved areas, to predict DME.

Optical coherence tomography in the management of diabetic macular oedema.

Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss. This classification forms the basis of management strategies of DMO. Despite years of research on quantitative and qualitative DMO related features assessed by OCT, these do not fully inform physicians of the prognosis and severity of DMO relative to visual function. Having said that, recent research on novel OCT biomarkers development and re-defined classification of DMO show better correlation with visual function and treatment response. This review summarises the current evidence of the association of OCT biomarkers in DMO management and its potential clinical importance in predicting VA and anatomical treatment response. The review also discusses some future directions in this field, such as the use of artificial intelligence to quantify and monitor OCT biomarkers and retinal fluid and identify phenotypes of DMO, and the need for standardisation and classification of OCT biomarkers to use in future clinical trials and clinical practice settings as prognostic markers and secondary treatment outcome measures in the management of DMO.

From diagnosis to prognosis: A paradigm shift for multimodal imaging in assessing diabetic macular edema.

Diabetic macular edema (DME) is one of the leading causes of visual impairment in patients with diabetes. Multimodal imaging (MMI) has allowed a shift from DME diagnosis to prognosis. Although there are no accepted guidelines, MMI may also lead to treatment customization. Several study groups have tried to identify structural biomarkers that can predict treatment response and long-term visual prognosis. The purpose of this editorial is to review currently proposed optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) biomarkers.

Optical coherence tomography angiography analysis of choroidal microvasculature in various forms of diabetic macular edema.

INTRODUCTION: Optical coherence tomography angiography (OCTA) research in diabetic macular edema (DME) has focused on the retinal microvasculature with little attention to the choroid. The goal of this study was to analyze the association between quantitative choroidal OCTA parameters and various forms of DME observed on optical coherence tomography. METHODS: We conducted a retrospective study of 61 eyes of 53 patients with DME. DME was classified as early or advanced, and as sponge-like diffuse retinal thickening (DRT), cystoid macular edema (CME) or serous retinal detachment (SRD). Quantitative OCTA parameters (vessel density [VD] in the superficial capillary plexus [SCP], middle capillary plexus [MCP], deep capillary plexus [DCP] and choriocapillaris [CC]) were recorded. RESULTS: The VD in the CC and SCP was significantly higher in patients with early DME compared to patients with advanced DME (P value<0.01). CC VD was lower in subjects with SRD compared to DRT and CME (P value<0.001). Moreover, it was lower in CME compared to DRT (P value<0.05). No statistical differences were found between VD in the MCP and DCP (P value>0.05). Furthermore, CC VD was lower in patients with increased retinal thickness, disruption of the ellipsoid zone (EZ) or external limiting membrane (ELM), and disorganization of the inner retinal layers (DRIL) (P value<0.05). CONCLUSION: CC ischemia plays an important role in the pathogenesis of DME. We demonstrated a decrease in CC VD in patients with severe DME, SRD, retinal thickening, EZ and/or ELM disruption and DRIL.

Imaging the Retinal Vascular Mural Cells In Vivo: Elucidating the Timeline of Their Loss in Diabetic Retinopathy.

BACKGROUND: Vascular mural cells (VMCs) are integral components of the retinal vasculature with critical homeostatic functions such as maintaining the inner blood-retinal barrier and vascular tone, as well as supporting the endothelial cells. Histopathologic donor eye studies have shown widespread loss of pericytes and smooth muscle cells, the 2 main VMC types, suggesting these cells are critical to the pathogenesis of diabetic retinopathy (DR). There remain, however, critical gaps in our knowledge regarding the timeline of VMC demise in human DR. METHODS: In this study, we address this gap using adaptive optics scanning laser ophthalmoscopy to quantify retinal VMC density in eyes with no retinal disease (healthy), subjects with diabetes without diabetic retinopathy, and those with clinical DR and diabetic macular edema. We also used optical coherence tomography angiography to quantify capillary density of the superficial and deep capillary plexuses in these eyes. RESULTS: Our results indicate significant VMC loss in retinal arterioles before the appearance of classic clinical signs of DR (diabetes without diabetic retinopathy versus healthy, 5.0±2.0 versus 6.5±2.0 smooth muscle cells per 100 µm; P<0.05), while a significant reduction in capillary VMC density (5.1±2.3 in diabetic macular edema versus 14.9±6.0 pericytes per 100 µm in diabetes without diabetic retinopathy; P=0.01) and capillary density (superficial capillary plexus vessel density, 37.6±3.8 in diabetic macular edema versus 45.5±2.4 in diabetes without diabetic retinopathy; P<0.0001) is associated with more advanced stages of clinical DR, particularly diabetic macular edema. CONCLUSIONS: Our results offer a new framework for understanding the pathophysiologic course of VMC compromise in DR, which may facilitate the development and monitoring of therapeutic strategies aimed at VMC preservation and potentially the prevention of clinical DR and its associated morbidity. Imaging retinal VMCs provides an unparalleled opportunity to visualize these cells in vivo and may have wider implications in a range of diseases where these cells are disrupted.

Predicting OCT biological marker localization from weak annotations.

Recent developments in deep learning have shown success in accurately predicting the location of biological markers in Optical Coherence Tomography (OCT) volumes of patients with Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). We propose a method that automatically locates biological markers to the Early Treatment Diabetic Retinopathy Study (ETDRS) rings, only requiring B-scan-level presence annotations. We trained a neural network using 22,723 OCT B-Scans of 460 eyes (433 patients) with AMD and DR, annotated with slice-level labels for Intraretinal Fluid (IRF) and Subretinal Fluid (SRF). The neural network outputs were mapped into the corresponding ETDRS rings. We incorporated the class annotations and domain knowledge into a loss function to constrain the output with biologically plausible solutions. The method was tested on a set of OCT volumes with 322 eyes (189 patients) with Diabetic Macular Edema, with slice-level SRF and IRF presence annotations for the ETDRS rings. Our method accurately predicted the presence of IRF and SRF in each ETDRS ring, outperforming previous baselines even in the most challenging scenarios. Our model was also successfully applied to en-face marker segmentation and showed consistency within C-scans, despite not incorporating volume information in the training process. We achieved a correlation coefficient of 0.946 for the prediction of the IRF area.

Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.

Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

- Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME). METHODS: Prospective evaluation of OCT images of DME (n = 320) subject to elastic transformation, with the deformation intensity represented by ([Formula: see text]). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of ([Formula: see text]), including low-, medium- and high-degree of augmentation; ([Formula: see text] = 1-6), ([Formula: see text] = 7-12), and ([Formula: see text] = 13-18), respectively. Three retina specialists evaluated all datasets in a blinded manner and designated each image as 'original' versus 'modified'. The rate of assignment of 'original' value to modified images (false-negative) was determined for each grader in each dataset. RESULTS: The false-negative rates ranged between 71-77% for the low-, 63-76% for the medium-, and 50-75% for the high-augmentation categories. The corresponding rates of correct identification of original images ranged between 75-85% ([Formula: see text]0.05) in the low-, 73-85% ([Formula: see text]0.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% ([Formula: see text]) in the high-augmentation categories. In the subcategory ([Formula: see text] = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% ([Formula: see text]0.05 for all graders). CONCLUSIONS: Deformation of low-medium intensity ([Formula: see text] = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement-Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.

INTEGRATING MACULAR OPTICAL COHERENCE TOMOGRAPHY WITH ULTRAWIDE-FIELD IMAGING IN A DIABETIC RETINOPATHY TELEMEDICINE PROGRAM USING A SINGLE DEVICE.

PURPOSE: To determine the effect of combined macular spectral-domain optical coherence tomography (SD-OCT) and ultrawide field retinal imaging (UWFI) within a telemedicine program. METHODS: Comparative cohort study of consecutive patients with both UWFI and SD-OCT. Ultrawide field retinal imaging and SD-OOCT were independently evaluated for diabetic macular edema (DME) and nondiabetic macular abnormality. Sensitivity and specificity were calculated with SD-OCT as the gold standard. RESULTS: Four hundred twenty-two eyes from 211 diabetic patients were evaluated. Diabetic macular edema severity by UWFI was as follows: no DME 93.4%, noncenter involved DME (nonciDME) 5.1%, ciDME 0.7%, ungradable DME 0.7%. SD-OCT was ungradable in 0.5%. Macular abnormality was identified in 34 (8.1%) eyes by UWFI and in 44 (10.4%) eyes by SD-OCT. Diabetic macular edema represented only 38.6% of referable macular abnormality identified by SD-OCT imaging. Sensitivity/specificity of UWFI compared with SD-OCT was 59%/96% for DME and 33%/99% for ciDME. Sensitivity/specificity of UWFI compared with SDOCT was 3%/98% for epiretinal membrane. CONCLUSION: Addition of SD-OCT increased the identification of macular abnormality by 29.4%. More than 58.3% of the eyes believed to have any DME on UWF imaging alone were false-positives by SD-OCT. The integration of SD-OCT with UWFI markedly increased detection and reduced false-positive assessments of DME and macular abnormality in a teleophthalmology program.

Evaluation of Posterior Ocular Blood Flow in Diabetic Retinopathy Patients Without Macular Edema Using Optical Coherence Tomography Angiography.

AIM: The objective of this study is to investigate and compare the superficial and deep vascular structures of the retina, as well as the changes in the choriocapillaris (CC) and optic disc microvasculature, using optical coherence tomography angiography (OCTA) in patients diagnosed with diabetes mellitus (DM) without diabetic retinopathy (DR), patients with non-proliferative and proliferative DR, and healthy individuals. MATERIALS AND METHODS: This prospective study conducted between July 2020 and July 2021 included patients diagnosed with type 2 DM without DR, as well as patients with mild nonproliferative, moderate nonproliferative, and proliferative DR without macular oedema. A control group of 25 age- and gender-matched healthy individuals was also included. OCTA parameters of the patients were examined. RESULTS: In the DR groups, compared to the control group, there was a significant decrease in macular superficial, deep, and CC perfusion areas as the severity of DR increased (p<0.001). The vascular density (VD) of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) exhibited a statistically significant decrease in all quadrants of the DR group compared to the control group (p = 0.033 for SCP in the fovea, p<0.001 for all other quadrants). The superficial and deep FAZs showed a significant expansion in the DR group compared to the control group (p = 0.003 for superficial FAZ, p<0.001 for deep FAZ). As the severity of DR increased, there was a statistically significant decrease in the perfusion areas of the optic nerve head (ONH), radial peripapillary capillary (RPC), and vitreous segments (p<0.001 for ONH, p = 0.031 for RPC, p<0.001 for vitreous). There was a statistically significant decrease in RPC VD in all quadrants as the severity of DR increased. Moreover, as the severity of DR increased, a statistically significant decrease in the VD of the ONH was observed in all quadrants except for the inferior nasal (p = 0.094), inferior temporal (p = 0.111), superior temporal (p = 0.18), and temporal (p = 0.284) quadrants. CONCLUSION: Our study demonstrated the involvement of macular and optic nerve perfusion areas (PA) and VD in diabetic patients. OCTA proved to be a valuable and noninvasive imaging modality, providing an easy and repeatable assessment of posterior segment vascular changes in patients with DR.

Local ocular factors associated with the development of diabetic macular edema: an inter-eye study.

To investigate local ocular factors associated with the development of diabetic macular edema (DME), we classified each eye of patients with unilateral DME as the DME eyes or the fellow eyes (without DME). We compared the clinical characteristics, optical coherence tomography (OCT), and OCT angiography (OCTA), ultra-wide field fundus photography, and angiography features of each eye. As a result, fifty-five patients with unilateral DME were enrolled. Although the diabetic retinopathy stage was not different between each group of eyes, DME eyes showed a higher prevalence of venous beading and a larger area of nonperfusion region than did fellow eyes (all P < 0.05). OCTA features of DME eyes also showed a larger foveal avascular zone in the deep capillary plexus and a lower vascular density in both the superficial and deep capillary plexuses (all P < 0.05). This study highlighted ocular features reflecting retinal ischemia, such as venous beading, area of nonperfusion region, and vascular density in the central retinal area, are associated with the development of DME. OCTA and ultra-wide field fluorescein angiography may be useful for evaluating the parameters of retinal ischemia and the risk of DME development.

Distribution of leakage index using ultra-widefield fluorescein angiography in patients with non-ischemic branch retinal vein occlusion and its association with macular edema.

BACKGROUND: To investigate the distribution of leakage index in patients with non-ischemic branch retinal vein occlusion (BRVO) and its correlation with the severity of macular edema. METHODS: Retrospective observational study. Forty-five eyes of 45 patients with BRVO were included. Late ultra-widefield fluorescein angiography images of the affected eyes were processed and analyzed for their leakage index using Fiji software. The visible panretinal area was further divided into the peri­macular area (PMA), near-peripheral area (NPA), midperipheral area (MPA), and far-peripheral area (FPA). The relationship between the leakage index and central retinal thickness (CMT) was analyzed for the panretina and each subregion. RESULTS: The median (interquartile range) leakage indexes of the panretina, PMA, NPA, MPA, and FPA were 5.532% (7.667%), 23.127% (26.073%), 8.303% (16.807%), 1.588% (6.204%), and 0.408% (2.215%), respectively, with a mean CMT of 552.800 ± 183.335 µm. The CMT was positively correlated with the leakage index in the panretina, PMA, NPA, MPA and FPA (r = 0.468, 0.426, 0.463, 0.447, 0.320, respectively; all p < 0.05). CONCLUSIONS: The leakage index in non-ischemic BRVO patients is associated with macular edema severity. The leakage index has the potential to be a useful indicator for monitoring and guiding treatment of macular edema in BRVO patients.

Retinal Hydration Assessment With Optical Coherence Tomography: Unraveling Its Significance in Retinal Fluid Dynamics, Macular Edema and Cell Viability.

Purpose: The purpose of this study was to quantify retinal hydration (RH) levels with optical coherence tomography (OCT) and determine the extent of cellular damage resulting from intraretinal fluid alterations. Methods: We took 6.0 mm sections of the human sensory retina that were excised from 18 fresh (<24 hours) donor eyes. They were either exposed to various osmotic stresses between 90 and 305 mOsm or dehydrated under a laminar flow hood. Change in tissue weight was used to calculate the retinal water content (RWC). Image analyses were conducted on OCT between 0 and 180 minutes to assess retinal thickness (RT) and "optically empty areas" (OEAs) representing intraretinal fluid. Correlations were sought among RWC, OEA, RWC, and RT. The effect of RH on retinal cell viability (RCV) was assessed with the Live-Dead Assay. Results: RH demonstrated a stronger correlation with the OEA than plain RT measurements (r = 0.99, P < 0.001). RH-RCV interaction fits well to a bell-shaped curve. A significant proportion of retinal cells (>80%) remained viable despite the change in RH ranging between 0.87 and 1.42 times. This "safe zone" was found to be associated with a 22% increase in OEA (r = 0.99, P < 0.01). Conclusions: OCT has been demonstrated as a valuable tool for assessing RH and can be used for intraretinal fluid content analysis. RH is a better indicator of RCV compared with RT. Computing RH may improve the determination of functional outcome of intravitreal pharmacotherapeutics used for diabetic macular edema and exudative age-related macular degeneration. Translational Relevance: We link basic research and clinical care by assessing retinal hydration's impact on retinal fluid dynamics, macular edema, and cell viability.