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Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification", and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aims: To determine the presence of sectoral changes in vessel density (VD) patterns induced by vascular endothelial growth factor inhibitors (anti-VEGF) in patients with diabetic macular edema (DME) using optical coherence tomography angiography (OCTA)., Methods: Prospective, interventional study. A total of 43 patients (63 eyes) were initially enrolled in the study. We performed swept source (SS) OCT and sectorial OCTA measurement to determine parafoveal VD at baseline and after six months of anti-VEGF treatment. In the locations with statistically significant differences in VD between baseline and month 6, we performed univariate and multivariate analyses to determine which, if any, of the baseline variables were associated with the observed changes., Results: A total of 34 patients (48 eyes) were included in the final analysis. Mean VD decreased from baseline to month 6 (from 45.2 (± 3.5) to 44.6 (± 3.2) % in the SCP and from 50 (± 3.3) to 49 (± 3.9) % in the DCP). The only significant changes in VD were observed in the nasal sector of the deep capillary plexus, with a decrease of 2.9% (p = 0.001). On univariate and multivariate analyses, the only variable significantly associated with changes in VD in the nasal sector after 6 months of treatment was baseline VD in the same sector., Conclusions: Anti-VEGF therapy has a small impact on VD values over time. These variations observed after treatment seems to be related to changes over areas of vascular anomalies and displaced vessels adjacent to cystic areas, with no significant changes over ischemic areas. No correlation was observed between this trend and other clinical baseline features., (© 2024. The Author(s).)

Diabetic macular edema (DME), defined as retinal thickening near, or involving the fovea caused by fluid accumulation in the retina, can lead to vision impairment and blindness in patients with diabetes. Current knowledge of retina anatomy and function and DME pathophysiology has taken great advantage of the availability of several techniques for visualizing the retina. Combining these techniques in a multimodal imaging approach to DME is recommended to improve diagnosis and to guide treatment decisions. We review the recent literature about the following retinal imaging technologies: optical coherence tomography (OCT), OCT angiography (OCTA), wide-field and ultrawide-field techniques applied to fundus photography, fluorescein angiography, and OCTA. The emphasis will be on characteristic DME features identified by these imaging technologies and their potential or established role as diagnostic, prognostic, or predictive biomarkers. The role of artificial intelligence in the assessment and interpretation of retina images is also discussed., Competing Interests: Declaration of Competing Interest MOG: consultant and travel grant: Bayer, Novartis, Roche; DV: consultant for Abbvie, Bayer, Novartis, Roche. MP reports personal fees from Abbvie, Novartis, Bayer, Roche, Zeiss outside the submitted work. GC, LDA, ML, MR, MCS, DV and SV declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

An important abnormality in Optical Coherence Tomography (OCT) images is Hyper-Reflective Foci (HRF). This anomaly can be interpreted as a biomarker of serious retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) or the progression of disease from an early stage to a late one. In this paper, a new method is proposed for the identification of HRFs. The new method divides the OCT B-scan into patches and separately verifies each patch to determine whether or not the patch contains an HRF. The procedure of patch verification contains a texture-based framework which assigns appropriate labels according to intensity changes to each column and row. Then, a feature vector is extracted for each patch based on the assigned labels. The feature vectors are utilized in the training step of well-known classifiers like Support Vector Machine (SVM). Then, the classifiers are used to produce the labels for the test OCT images. The new method is evaluated on a public dataset including HRF labels. The experimental results show that the new method is capable of providing outstanding results in terms of speed and accuracy., (© 2024. The Author(s).)

Purpose: Dome-shaped macula (DSM) is known to occur in highly myopic adults and, recently, preterm infants. This study uses investigational handheld swept-source optical coherence tomography (SS-OCT) to further characterize infantile DSM., Methods: In this prospective, observational study, preterm infants undergoing retinopathy of prematurity screening and full-term infants within 72 hours of birth were imaged. Two trained graders assessed macular features, including DSM, subretinal fluid, and macular edema. A semi-automated program measured foveal immaturity, dome height, and diameter., Results: Two hundred seventeen imaging sessions from 50 full-term and 30 preterm infants were included (46% female, preterm birth weight 1038 ± 335 g, and gestational age 28.7 ± 3.1 weeks). DSM occurred in 40% preterm versus 14% full-term infants (P = 0.01). Mean postmenstrual age at first DSM diagnosis was 38.4 ± 0.0 weeks among preterm and 40.4 ± 1.1 weeks among full-term infants (P < 0.001). Dome height and diameter measured 55.67 ± 44.22 µm and 3583.15 ± 1090.35 µm for preterm versus 88.37 ± 44.73 µm and 3581.97 ± 355.07 µm for full-term infants (P = 0.24 and P = 0.96, respectively). All 27 images (11 preterm and 7 full-term infants) with 3-dimensional analysis had round dome configuration. No other associations were seen, including macular fluid (P = 0.17)., Conclusions: Infants frequently exhibit DSM without an association with macular fluid. Preterm infants were more likely than full-term infants to have DSM. Unlike DSM in children and adults, infantile DSM configuration is mostly round rather than ridge-shaped.

Background: Optical coherence tomography angiography (OCTA) is a relatively new extension of Optical coherence tomography (OCT) that generates non-invasive, depth-resolved images of the retinal microvasculature which allows for the detection of various features of diabetic retinopathy., Objectives: This study aimed to detect biomarkers that may predict an early anatomical response to the treatment of diabetic macular edema (DME) with intravitreal ranibizumab (IVR) by means of OCTA., Patients and Methods: This prospective interventional study was undertaken on 111 eyes of 102 naïve participants who had diabetic macular edema; enrolled patients were evaluated by taking a complete ophthalmologic history, examination and investigations by use of a pre-designed checklist involving Optical Coherence Tomography Angiography., Results: Regarding the best corrected visual acuity (BCVA) the Mean ± SD was 0.704 ± 0.158 preoperatively and 0.305 ± 0.131 postoperatively in good responder patients; and was 0.661 ± 0.164 preoperatively and 0.54 ± 0.178 postoperatively in poor responders. The central macular thickness (CMT) was 436.22 ± 54.66 μm preoperatively and 308.12 ± 33.09 μm postoperatively in good responder patients; and was 387.74 ± 44.05 μm preoperatively and 372.09 ± 52.86 μm postoperatively in poor responders. By comparing the pre injection size of the foveal avascular zone area (FAZ-A) in both groups, it found that the mean ± SD of FAZ-A was 0.297 ± 0.038 mm in good responder patients compared to 0.407 ± 0.05 mm in non-responder patients. The preoperative superficial capillary plexus (SCP) foveal vascular density (VD) was 24.02 ± 3.01% in good responder patients versus 17.89 ± 3.19% um in poor responders. The preoperative SCP parafoveal VD was 43.06 ± 2.67% in good responder patients versus 37.96 ± 1.82% um in poor responders. The preoperative deep capillary plexus (DCP) foveal VD was 30.58 ± 2.89% in good responder patients versus 25.45 ± 3.14% in poor responders. The preoperative DCP parafoveal VD was 45.66 ± 2.21% in good responder patients versus 43.26 ± 2.35% um in poor responders, this was statistically significant., Conclusion: OCTA offers an accurate measurement for VD in the macula as well as the FAZ-A which could be used to predict an early anatomical response of anti-VEGF treatment in DME., (© 2024. The Author(s).)

Background: We aimed to evaluate microaneurysms (MAs) after treatment with anti-vascular endothelial growth factor (anti-VEGF) therapy to understand causes of chronic edema and anti-VEGF resistance., Methods: Patients with non-proliferative diabetic retinopathy, with or without macular edema were recruited. Optical coherence tomography angiography (OCTA) MAs-related parameters were observed, including the maximum diameter of overall dimensions, material presence, and flow signal within the lumen. OCTA parameters also included central macular thickness (CMT), foveal avascular zone, superficial and deep capillary plexuses, and non-flow area measurements on the superficial retinal slab., Results: Overall, 48 eyes from 43 patients were evaluated. CMT differed significantly between the diabetic macular edema (DME ) and non-DME (NDME) groups at 1st, 2nd, 3rd, and 6th months of follow-up (P < 0.001; <0.001; 0.003; <0.001, respectively). A total of 55 and 59 MAs were observed in the DME (mean = 99.40 ± 3.18 μm) and NDME (mean maximum diameter = 74.70 ± 2.86 μm) groups at baseline, respectively (significant between-group difference: P < 0.001). Blood flow signal was measurable for 46 (83.6%) and 34 (59.3%) eyes in the DME and NDME groups, respectively (significant between-group difference: P < 0.001)., Conclusions: Compared to the NDME group, the DME group had larger MAs and a higher blood-flow signal ratio. Following anti-VEGF therapy, changes in the diameter of MAs were observed before changes in CMT thickness., (© 2024. The Author(s).)

This study was intended to investigate the macular vascular and photoreceptor changes for diabetic macular edema (DME) at the early stage. A total of 255 eyes of 134 diabetes mellitus patients were enrolled and underwent an ophthalmological and systemic evaluation in this cross-sectional study. Early DME was characterized by central subfoveal thickness (CST) value between 250 and 325 μm, intact ellipsoid zone, and an external limiting membrane. While non-DME was characterized by CST < 250 μm with normal retinal morphology and structure. Foveal avascular zone (FAZ) area ≤ 0.3 mm 2 (P < 0.001, OR = 0.41, 95% CI 0.26-0.67 in the multivariate analysis) and HbA1c level ≤ 8% (P = 0.005, OR = 0.37, 95% CI 0.19-0.74 in multivariate analysis) were significantly associated with a higher risk of early DME. Meanwhile, no significant differences exist in cone parameters between non-DME and early DME eyes. Compared with non-DME eyes, vessel diameter, vessel wall thickness, wall-to-lumen ratio, the cross-sectional area of the vascular wall in the upper side were significantly decreased in the early DME eyes (P = 0.001, P < 0.001, P = 0.005, P = 0.003 respectively). This study suggested a vasospasm or vasoconstriction with limited further photoreceptor impairment at the early stage of DME formation. CST ≥ 250 μm and FAZ ≤ 0.3 mm 2 may be the indicator for early DME detection., (© 2024. The Author(s).)

Purpose: To assess the feasibility of generating synthetic fluorescein angiography (FA) images from color fundus (CF) images using pixel-to-pixel generative adversarial network (pix2pixGANs) for clinical applications. Research questions addressed image realism to retinal specialists and utility for assessing macular edema (ME) in Retinal Vein Occlusion (RVO) eyes., Methods: We used a registration-guided pix2pixGANs method trained on the CF-FA dataset from Kham Eye Centre, Kandze Prefecture People's Hospital. A visual Turing test confirmed the realism of synthetic images without novel artifacts. We then assessed the synthetic FA images for assessing ME. Finally, we quantitatively evaluated the synthetic images using Fréchet Inception distance (FID) and structural similarity measures (SSIM)., Results: The raw development dataset had 881 image pairs from 349 subjects. Our approach is capable of generating realistic FA images because small vessels are clearly visible and sharp within one optic disc diameter around the macula. Two retinal specialists agreed that more than 85% of synthetic FA images have good or excellent image quality. For ME detection, accuracy was similar for real and synthetic images. FID demonstrated a 38.9% improvement over the previous state-of-the-art (SOTA), and SSIM reached 0.78 compared to the previous SOTA's 0.67., Conclusions: We developed a pix2pixGANs model translating FA images from label-free CF images, yielding reliable synthetic FA images. This suggests potential for noninvasive evaluation of ME in RVO eyes using pix2pix GANs techniques., Translational Relevance: Pix2pixGANs techniques have the potential to assist in the noninvasive clinical assessment of ME in RVO eyes.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual image analysis by experts, which can be time-consuming and costly due to the scarcity of such experts. To overcome the aforementioned challenges, we present the Modified CornerNet approach with DenseNet-100. This system aims to localize and classify lesions associated with DR and DME. To train our model, we first generate annotations for input samples. These annotations likely include information about the location and type of lesions within the retinal images. DenseNet-100 is a deep CNN used for feature extraction, and CornerNet is a one-stage object detection model. CornerNet is known for its ability to accurately localize small objects, which makes it suitable for detecting lesions in retinal images. We assessed our technique on two challenging datasets, EyePACS and IDRiD. These datasets contain a diverse range of retinal images, which is important to estimate the performance of our model. Further, the proposed model is also tested in the cross-corpus scenario on two challenging datasets named APTOS-2019 and Diaretdb1 to assess the generalizability of our system. According to the accomplished analysis, our method outperformed the latest approaches in terms of both qualitative and quantitative results. The ability to effectively localize small abnormalities and handle over-fitted challenges is highlighted as a key strength of the suggested framework which can assist the practitioners in the timely recognition of such eye ailments., (© 2024. International Federation for Medical and Biological Engineering.)

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%., (© 2024. The Author(s).)

Deep learning techniques were used in ophthalmology to develop artificial intelligence (AI) models for predicting the short-term effectiveness of anti-VEGF therapy in patients with macular edema secondary to branch retinal vein occlusion (BRVO-ME). 180 BRVO-ME patients underwent pre-treatment FFA scans. After 3 months of ranibizumab injections, CMT measurements were taken at baseline and 1-month intervals. Patients were categorized into good and poor prognosis groups based on macular edema at the 4th month follow-up. FFA-Net, a VGG-based classification network, was trained using FFA images from both groups. Class activation heat maps highlighted important locations. Benchmark models (DesNet-201, MobileNet-V3, ResNet-152, MansNet-75) were compared for training results. Performance metrics included accuracy, sensitivity, specificity, F1 score, and ROC curves. FFA-Net predicted BRVO-ME treatment effect with an accuracy of 88.63% and an F1 score of 0.89, with a sensitivity and specificity of 79.40% and 71.34%, respectively.The AUC of the ROC curve for the FFA-Net model was 0.71. The use of FFA based on deep learning technology has feasibility in predicting the treatment effect of BRVO-ME. The FFA-Net model constructed with the VGG model as the main body has good results in predicting the treatment effect of BRVO-ME. The typing of BRVO in FFA may be an important factor affecting the prognosis., (© 2024. The Author(s).)

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration., (© 2024. The Author(s).)

Background: Diabetes can cause chronic microvascular complications such as diabetic retinopathy (DR) and diabetic nephropathy (DN). DR and DN can lead to or exacerbate diabetic macular edema (DME). Hemodialysis (HD) is the main treatment method for patients with end-stage kidney disease (ESKD) secondary to DN., Purpose: The aim of this prospective cohort study was to determine the immediate effect of single HD session on retinal and choroidal thickness in DR patients with ESKD and the features of DR and the prevalence of DME in these patients who have received long-term HD., Methods: Eighty-five eyes of 44 DR patients with ESKD who underwent long-term HD were examined by swept-source optical coherence tomography angiography (SS-OCTA). Based on OCTA images, the characteristics of DR and the prevalence of DME in these patients were analyzed. Changes in central retinal thickness (CRT), central retinal volume (CRV), subfoveal choroidal thickness (SFCT) and subfoveal choroidal volume (SFCV) within 30 min before and after single HD session were compared. CRT, CRV, SFCT and SFCV were compared before single HD session and before the next single HD session., Results: There was no significant difference in the average CRT (251.69 ± 39.21 μm vs. 251.46 ± 39.38 μm, P = 0.286) or CRV (0.15 ± 0.62 μm vs. 0.15 ± 0.63 μm, P = 0.324) between before and after single HD session. After single HD session, SFCT (243.11 ± 77.15 μm vs. 219.20 ± 72.84 μm, P < 0.001) and SFCV (0.15 ± 0.10 μm vs. 0.13 ± 0.90 μm, P < 0.001) significantly decreased. There was no statistically significant difference in CRT (251.69 ± 39.21 μm vs. 251.11 ± 38.47 μm, P = 0.206), CRV (0.15 ± 0.62 μm vs. 0.15 ± 0.61 μm, P = 0.154), SFCT (243.11 ± 77.15 μm vs. 245.41 ± 76.23 μm, P = 0.108), or SFCV (0.15 ± 0.10 μm vs. 0.16 ± 0.10 μm, P = 0.174) before HD and before the next single HD session. On en face OCTA images, eighty-five eyes (100%) had retinal nonperfusion areas, foveal avascular zone (FAZ) enlargement, and abnormal retinal microvasculature. Based on cross-sectional OCTA images, retinal neovascularization (RNV) was confirmed in 42 eyes (49.41%), and intraretinal microvascular abnormalities (IRMAs) were detected in 85 eyes (100%). Seventeen eyes (20%) still had DME, all of which were cystoid macular edema (CME). Among eyes with DME, the epiretinal membrane (ERM) was present in 7 eyes (8.24%)., Conclusions: For DR patients with ESKD who have undergone long-term HD, the choroidal thickness still changes significantly before and after single HD session, which may be related to short-term effects such as reduced blood volume and plasma osmotic pressure caused by single HD session. Although macular features seem to have stabilized in DR patients undergoing long-term dialysis, the DR of patients with ESKD should still be given attention., (© 2024. The Author(s).)

The effect of diabetes mellitus (DM) on individual retinal layers remains incompletely understood. We evaluated the intra-retinal layer thickness alterations in 71 DM eyes with no diabetic retinopathy (DR), 90 with mild DR, and 63 with moderate DR without macular edema, using spectral-domain optical coherence tomography (SD-OCT) and the Iowa Reference Algorithm for automated retinal layer segmentation. The average thickness of 10 intra-retinal layers was then corrected for ocular magnification using axial length measurements, and pairwise comparisons were made using multivariable linear regression models adjusted for gender and race. In DM no DR eyes, significant thinning was evident in the ganglion cell layer (GCL; p < 0.001), inner nuclear layer (INL; p = 0.001), and retinal pigment epithelium (RPE; p = 0.014) compared to normal eyes. Additionally, mild DR eyes exhibited a thinner inner plexiform layer (IPL; p = 0.008) than DM no DR eyes. Conversely, moderate DR eyes displayed thickening in the INL, outer nuclear layer, IPL, and retinal nerve fiber layer (all p ≤ 0.002), with notably worse vision. These findings highlight distinctive patterns: early diabetic eyes experience thinning in specific retinal layers, while moderate DR eyes exhibit thickening of certain layers and slightly compromised visual acuity, despite the absence of macular edema. Understanding these structural changes is crucial for comprehending diabetic eye complications., (© 2024. The Author(s).)

Purpose: To investigate the clinical value of epiretinal macrophage-like cells (eMLCs) in different stages of diabetic retinopathy (DR), including diabetic macular edema (DME)., Methods: One hundred and seventy-six eyes of 176 patients, including 31 diabetes eyes without retinopathy, 24 with mild nonproliferative DR (NPDR), 37 with moderate NPDR, 32 with severe NPDR, and 52 with proliferative DR, were compared with 30 healthy controls. The optic nerve head and macular region were examined using en face optical coherence tomography and optical coherence tomography angiography. A previously described technique was employed to measure the eMLCs on the inner limiting membrane., Results: The optic nerve head and macular eMLC densities increased with worsening DR stage and were higher in moderate, severe NPDR, and proliferative DR, especially in eyes with DME (all adjusted P < 0.01). The optic nerve head and macular eMLC densities in the DME groups were higher than those in their corresponding non-DME groups at the same DR stage (all P < 0.05). The average macular thickness was correlated with macular eMLC density (standardized β = 0.661, P < 0.001) in patients with diabetes. The proportion of eyes with larger and plumper eMLCs increased with worsening DR and was higher in moderate, severe NPDR, proliferative DR, and eyes with DME ( P = 0.018, P < 0.001, P < 0.001, and P < 0.001, respectively)., Conclusion: Increased density along with morphological changes of eMLCs is observed beginning with moderate NPDR and correlates with the progression of DR, including DME.

Purpose: To investigate the frequency and type of artifacts on OCT angiography (OCTA) images and the relationship with clinical features in eyes with diabetic macular edema (DME)., Design: Retrospective, cross-sectional comparative study., Subjects: One hundred ninety-two eyes of 140 patients with DME were included., Methods: Medical records, OCT and OCTA images (Spectralis), and ultrawidefield color fundus photographs (Optos plc) were evaluated., Main Outcome Measures: The frequency of artifact types (segmentation, motion, projection artifact, and low signal) was determined. The relationships between artifact types and clinical features such as best-corrected visual acuity (BCVA), mean central retinal thickness (CRT), foveal avascular zone (FAZ) area, perimeter, circularity index, perfusion density (PD), vessel density (VD), fractal dimension (FD) in the superficial capillary plexus, intermediate capillary plexus (ICP), and deep capillary plexus (DCP), flow voids (FVs) in the choriocapillaris, presence of hard exudate (HE), and cataract were determined., Results: The mean age was 71.6 ± 11.4 years, and 86 (61.4%) out of 140 were men. Artifacts were present in 63 (32.8%) of 192 eyes. Twenty-nine (15.1%) eyes had segmentation artifacts, 12 (6.3%) had motion artifacts, 11 (5.7%) had projection artifacts, and 18 (9.4%) had low signal. Best-corrected visual acuity, PD, VD, and FD in ICP and DCP were significantly lower; and CRT, FAZ area and perimeter in ICP and DCP, and presence of cystoid macular edema, HE, and cataract were higher in eyes with artifacts versus eyes without artifacts (P < 0.05 for each). Multivariate linear regression analysis showed a significant association between segmentation artifacts and decreased BCVA (odds ratio [OR], 5.277; P = 0.02), increased CRT (OR, 1.015; P < 0.001), increased area of FAZ in DCP (OR, 6.625; P = 0.02), and increased perimeter of FAZ in DCP (OR, 1.775; P < 0.04); there was also a significant association between projection artifacts and presence of HE (OR, 2.017; P = 0.02) and between motion artifacts and presence of cataract (OR, 4.102; P = 0.01)., Conclusions: OCT angiography artifacts were present in one third of DME eyes, with segmentation artifacts being the most frequent type. Determining OCTA artifacts is crucial to ensure accurate clinical evaluation. These data could help in developing more standardized clinical protocols for image acquisition and interpretation used in clinical practice and research., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection bias, we stacked convolutional and recurrent neural networks (CNN-RNN) to analyze complete optical coherence tomography (OCT) cubes in a row and predict diabetic macular edema (DME), in a real-world diabetic retinopathy screening program. A retrospective cohort study was carried out. Throughout 4-years, 5314 OCT cubes from 4408 subjects who attended to the diabetic retinopathy (DR) screening program were included. We arranged twenty-two (22) pre-trained CNNs in parallel with a bidirectional RNN layer stacked at the bottom, allowing the model to make a prediction for the whole OCT cube. The staff of retina experts built a ground truth of DME later used to train a set of these CNN-RNN models with different configurations. For each trained CNN-RNN model, we performed threshold tuning to find the optimal cut-off point for binary classification of DME. Finally, the best models were selected according to sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) with their 95% confidence intervals (95%CI). An ensemble of the best models was also explored. 5188 cubes were non-DME and 126 were DME. Three models achieved an AUROC of 0.94. Among these, sensitivity, and specificity (95%CI) ranged from 84.1-90.5 and 89.7-93.3, respectively, at threshold 1, from 89.7-92.1 and 80-83.1 at threshold 2, and from 80.2-81 and 93.8-97, at threshold 3. The ensemble model improved these results, and lower specificity was observed among subjects with sight-threatening DR. Analysis by age, gender, or grade of DME did not vary the performance of the models. CNN-RNN models showed high diagnostic accuracy for detecting DME in a real-world setting. This engine allowed us to detect extra-foveal DMEs commonly overlooked in other studies, and showed potential for application as the first filter of non-referable patients in an outpatient center within a population-based DR screening program, otherwise ended up in specialized care., (© 2024. The Author(s).)

The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We collected a dataset composed of 442 OCT images on which we annotated 6847 HE and the presence of DRIL. A complex operational pipeline was defined to implement data cleaning and image transformations, and train two DL models. The state-of-the-art neural network architectures (Yolov7, ConvNeXt, RegNetX) and advanced techniques were exploited to aggregate the results (Ensemble learning, Edge detection) and obtain a final model. The DL approach reached good performance in detecting HE and classifying DRIL. Regarding HE detection the model got an AP@0.5 score equal to 34.4% with Precision of 48.7% and Recall of 43.1%; while for DRIL classification an Accuracy of 91.1% with Sensitivity and Specificity both of 91.1% and AUC and AUPR values equal to 91% were obtained. The P-value was lower than 0.05 and the Kappa coefficient was 0.82. The DL models proved to be able to identify HE and DRIL in eyes with DME with a very good accuracy and all the metrics calculated confirmed the system performance. Our DL approach demonstrated to be a good candidate as a supporting tool for ophthalmologists in OCT images analysis., (© 2024. The Author(s).)

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., (© 2024 IOP Publishing Ltd.)

To compare two screening strategies for diabetic retinopathy (DR), and to determine the health-economic impact of including optical coherence tomography (OCT) in a regular DR screening. This cross-sectional study included a cohort of patients (≥ 18 years) with type 1 or 2 diabetes mellitus (T1D or T2D) from a pilot DR screening program at Oslo University Hospital, Norway. A combined screening strategy where OCT was performed in addition to fundus photography for all patients, was conducted on this cohort and compared to our existing sequential screening strategy. In the sequential screening strategy, OCT was performed on a separate day only if fundus photography indicated diabetic macular edema (DME). The presence of diabetic maculopathy on fundus photography and DME on OCT was determined by two medical retina specialists. Based on the prevalence rate of diabetic maculopathy and DME from the pilot, we determined the health-economic impact of the two screening strategies. The study included 180 eyes of 90 patients. Twenty-seven eyes of 18 patients had diabetic maculopathy, and of these, 7 eyes of 6 patients revealed DME on OCT. When diabetic maculopathy was absent on fundus photographs, OCT could not reveal DME. Accordingly, 18 patients (20%) with diabetic maculopathy would have needed an additional examination with OCT in the sequential screening strategy, 6 (33%) of whom would have had DME on OCT. In an extended healthcare perspective analysis, the cost of the sequential screening strategy was higher than the cost of the combined screening strategy. There was a weak association between diabetic maculopathy on fundus photography and DME on OCT. The health economic analysis suggests that including OCT as a standard test in DR screening could potentially be cost-saving., (© 2024. The Author(s).)

Purpose: The purpose of this study was to analyze optical coherence tomography (OCT) images of generative adversarial networks (GANs) for the prediction of diabetic macular edema after long-term treatment., Methods: Diabetic macular edema (DME) eyes (n = 327) underwent anti-vascular endothelial growth factor (VEGF) treatments every 4 weeks for 52 weeks from a randomized controlled trial (CRTH258B2305, KINGFISHER) were included. OCT B-scan images through the foveal center at weeks 0, 4, 12, and 52, fundus photography, and retinal thickness (RT) maps were collected. GAN models were trained to generate probable OCT images after treatment. Input for each model were comprised of either the baseline B-scan alone or combined with additional OCT, thickness map, or fundus images. Generated OCT B-scan images were compared with real week 52 images., Results: For 30 test images, 28, 29, 15, and 30 gradable OCT images were generated by CycleGAN, UNIT, Pix2PixHD, and RegGAN, respectively. In comparison with the real week 52, these GAN models showed positive predictive value (PPV), sensitivity, specificity, and kappa for residual fluid ranging from 0.500 to 0.889, 0.455 to 1.000, 0.357 to 0.857, and 0.537 to 0.929, respectively. For hard exudate (HE), they were ranging from 0.500 to 1.000, 0.545 to 0.900, 0.600 to 1.000, and 0.642 to 0.894, respectively. Models trained with week 4 and 12 B-scans as additional inputs to the baseline B-scan showed improved performance., Conclusions: GAN models could predict residual fluid and HE after long-term anti-VEGF treatment of DME., Translational Relevance: The implementation of this tool may help identify potential nonresponders after long-term treatment, thereby facilitating management planning for these eyes.

To improve the understanding of potential pathological mechanisms of macular edema (ME), we try to discover biomarker candidates related to ME caused by diabetic retinopathy (DR) and retinal vein occlusion (RVO) in spectral-domain optical coherence tomography images by means of deep learning (DL). 32 eyes of 26 subjects with non-proliferative DR (NPDR), 77 eyes of 61 subjects with proliferative DR (PDR), 120 eyes of 116 subjects with branch RVO (BRVO), and 17 eyes of 15 subjects with central RVO (CRVO) were collected. A DL model was implemented to guide biomarker candidate discovery. The disorganization of the retinal outer layers (DROL), i.e., the gray value of the retinal tissues between the external limiting membrane (ELM) and retinal pigment epithelium (RPE), the disrupted and obscured rate of the ELM, ellipsoid zone (EZ), and RPE, was measured. In addition, the occurrence, number, volume, and projected area of hyperreflective foci (HRF) were recorded. ELM, EZ, and RPE are more likely to be obscured in RVO group and HRFs are observed more frequently in DR group (all P ≤ 0.001). In conclusion, the features of DROL and HRF can be possible biomarkers related to ME caused by DR and RVO in OCT modality., (© 2024. The Author(s).)

Intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections have emerged as the most common therapeutic approach for the management of diabetic macular edema (DME). Despite their proven superiority over other interventions, there is a paucity of data regarding the relative effectiveness of anti-VEGF agents in treating DME diagnosed with different patterns of optical coherence tomography (OCT). In this regard, we conducted a systematic review and comparative analysis of the therapeutic efficacy of intravitreal bevacizumab, ranibizumab, aflibercept, and conbercept in the management of DME with diffuse retinal thickening (DRT), cystoid macular edema (CME), and serous retinal detachment (SRD) patterns identified using OCT. Our study encompassed a comprehensive search of PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Wan Fang Data from their inception until January 25, 2023. The network meta-analysis involved the inclusion of 1606 patients from 20 retrospective studies with a moderate risk of bias but no evidence of publication bias. The DRT group had the highest increase in best-corrected visual acuity (BCVA) with anti-VEGF, while the SRD group had the greatest reduction in Central Macular Thickness (CMT). Furthermore, conbercept, ranibizumab, and bevacizumab, respectively, showed the best treatment outcomes for patients with DRT, CME, and SRD in terms of improvement in BCVA. And, conbercept exhibited the highest reduction in CMT in the DRT, CME, and SRD groups. In conclusion, our study highlights the efficacy of anti-VEGF agents in the management of DME and provides valuable insights into the selection of anti-VEGF agents tailored to the individual needs of patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Both hypertension and diabetes are known to increase the wall-to-lumen ratio (WLR) of retinal arterioles, but the differential effects are unknown. Here, we study the timing and relative impact of hypertension versus diabetes on the WLR in diabetic retinopathy (DR) to address this unresolved question., Methods: This prospective cross-sectional study compared the retinal arteriolar WLR in 17 healthy eyes, 15 with diabetes but no apparent DR (DM no DR), and 8 with diabetic macular edema (DME) and either nonproliferative or proliferative DR. We imaged each arteriole using adaptive optics scanning laser ophthalmoscopy and measured the WLR using ImageJ. Multiple linear regression (MLR) was performed to estimate the effects of hypertension, diabetes, and age on the WLR., Results: Both subjects with DM no DR and subjects with DME had significantly higher WLR than healthy subjects (0.36 ± 0.08 and 0.42 ± 0.08 vs. 0.29 ± 0.07, 1-way ANOVA P = 0.0009). MLR in healthy subjects and subjects with DM no DR showed hypertension had the strongest effect (regression coefficient = 0.08, P = 0.009), whereas age and diabetes were not significantly correlated with WLR. MLR in all three groups together (healthy, DM no DR, and DME) showed diabetes had the strongest effect (regression coefficient = 0.05, P = 0.02), whereas age and hypertension were not significantly correlated with WLR., Conclusions: Hypertension may be an early driver of retinal arteriolar wall thickening in preclinical DR, independent of age or diabetes, whereas changes specific to DR may drive wall thickening in DME and later DR stages., Translational Relevance: We offer a framework for understanding the relative contributions of hypertension and diabetes on the vascular wall, and emphasize the importance of hypertension control early in diabetes even before DR onset.

Importance: Vision-language models (VLMs) are a novel artificial intelligence technology capable of processing image and text inputs. While demonstrating strong generalist capabilities, their performance in ophthalmology has not been extensively studied., Objective: To assess the performance of the Gemini Pro VLM in expert-level tasks for macular diseases from optical coherence tomography (OCT) scans., Design, Setting, and Participants: This was a cross-sectional diagnostic accuracy study evaluating a generalist VLM on ophthalmology-specific tasks using the open-source Optical Coherence Tomography Image Database. The dataset included OCT B-scans from 50 unique patients: healthy individuals and those with macular hole, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. Each OCT scan was labeled for 10 key pathological features, referral recommendations, and treatments. The images were captured using a Cirrus high definition OCT machine (Carl Zeiss Meditec) at Sankara Nethralaya Eye Hospital, Chennai, India, and the dataset was published in December 2018. Image acquisition dates were not specified., Exposures: Gemini Pro, using a standard prompt to extract structured responses on December 15, 2023., Main Outcomes and Measures: The primary outcome was model responses compared against expert labels, calculating F1 scores for each pathological feature. Secondary outcomes included accuracy in diagnosis, referral urgency, and treatment recommendation. The model's internal concordance was evaluated by measuring the alignment between referral and treatment recommendations, independent of diagnostic accuracy., Results: The mean F1 score was 10.7% (95% CI, 2.4-19.2). Measurable F1 scores were obtained for macular hole (36.4%; 95% CI, 0-71.4), pigment epithelial detachment (26.1%; 95% CI, 0-46.2), subretinal hyperreflective material (24.0%; 95% CI, 0-45.2), and subretinal fluid (20.0%; 95% CI, 0-45.5). A correct diagnosis was achieved in 17 of 50 cases (34%; 95% CI, 22-48). Referral recommendations varied: 28 of 50 were correct (56%; 95% CI, 42-70), 10 of 50 were overcautious (20%; 95% CI, 10-32), and 12 of 50 were undercautious (24%; 95% CI, 12-36). Referral and treatment concordance were very high, with 48 of 50 (96%; 95 % CI, 90-100) and 48 of 49 (98%; 95% CI, 94-100) correct answers, respectively., Conclusions and Relevance: In this study, a generalist VLM demonstrated limited vision capabilities for feature detection and management of macular disease. However, it showed low self-contradiction, suggesting strong language capabilities. As VLMs continue to improve, validating their performance on large benchmarking datasets will help ascertain their potential in ophthalmology.

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases., (© 2024. The Author(s).)

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts., (© 2024. The Author(s).)

Macular edema, a prevalent ocular complication observed in various retinal diseases, can lead to significant vision loss or blindness, necessitating accurate and timely diagnosis. Despite the potential of deep learning for segmentation of macular edema, challenges persist in accurately identifying lesion boundaries, especially in low-contrast and noisy regions, and in distinguishing between Inner Retinal Fluid (IRF), Sub-Retinal Fluid (SRF), and Pigment Epithelial Detachment (PED) lesions. To address these challenges, we present a novel approach, termed Semantic Uncertainty Guided Cross-Transformer Network (SuGCTNet), for the simultaneous segmentation of multi-class macular edema. Our proposed method comprises two key components, the semantic uncertainty guided attention module (SuGAM) and the Cross-Transformer module (CTM). The SuGAM module utilizes semantic uncertainty to allocate additional attention to regions with semantic ambiguity, improves the segmentation performance of these challenging areas. On the other hand, the CTM module capitalizes on both uncertainty information and multi-scale image features to enhance the overall continuity of the segmentation process, effectively minimizing feature confusion among different lesion types. Rigorous evaluation on public datasets and various OCT imaging device data demonstrates the superior performance of our proposed method compared to state-of-the-art approaches, highlighting its potential as a valuable tool for improving the accuracy and reproducibility of macular edema segmentation in clinical settings, and ultimately aiding in the early detection and diagnosis of macular edema-related diseases and associated retinal conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Purpose: In diabetic macular edema (DME), hyper-reflective foci (HRF) has been linked to disease severity and progression. Using an automated approach, we aimed to investigate the baseline distribution of HRF in DME and their co-localization with cystoid intraretinal fluid (IRF)., Methods: Baseline spectral-domain optical coherence tomography (SD-OCT) volume scans (N = 1527) from phase III clinical trials YOSEMITE (NCT03622580) and RHINE (NCT03622593) were segmented using a deep-learning-based algorithm (developed using B-scans from BOULEVARD NCT02699450) to detect HRF. The HRF count and volume were assessed. HRF distributions were analyzed in relation to best-corrected visual acuity (BCVA), central subfield thickness (CST), and IRF volume in quartiles, and Diabetic Retinopathy Severity Scores (DRSS) in groups. Co-localization of HRF with IRF was calculated in the central 3-mm diameter using the en face projection., Results: HRF were present in most patients (up to 99.7%). Median (interquartile range [IQR]) HRF volume within the 3-mm diameter Early Treatment Diabetic Retinopathy Study ring was 1964.3 (3325.2) pL, and median count was 64.0 (IQR = 96.0). Median HRF volumes were greater with decreasing BCVA (nominal P = 0.0109), and increasing CST (nominal P < 0.0001), IRF (nominal P < 0.0001), and DRSS up to very severe nonproliferative diabetic retinopathy (nominal P < 0.0001). HRF co-localized with IRF in the en face projection., Conclusions: Using automated HRF segmentation of full SD-OCT volumes, we observed that HRF are a ubiquitous feature in DME and exhibit relationships with BCVA, CST, IRF, and DRSS, supporting a potential link to disease severity. The spatial distribution of HRF closely followed that of IRF.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception&#95;V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception&#95;V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception&#95;V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Purpose: to investigate the structural features and extended visual results in eyes affected by diabetic retinopathy (DR) and diabetic macular edema (DME) that have been successfully treated with anti-vascular endothelial growth factor (VEGF) therapy., Methods: Individuals (39 eyes of 39 patients) who had undergone long-term follow-up and demonstrated evidence of resolved DME after at least 2 years of follow-up following the initiation of anti-VEGF therapy were included. During the ""study visit"", structural OCT scans were examined to assess qualitative features indicative of neuroretina or retinal pigment epithelium distress. Additionally, a quantitative assessment of the inner and outer retinal thicknesses was conducted for topographical analysis., Results: The most robust qualitative association observed with BCVA at the "study visit" was linked to the presence of DRIL (p = 0.043) and the appearance of the ELM. (p = 0.045). Regarding quantitative parameters, a strong correlation was noted between the visual acuity during the "study visit" and the foveal and parafoveal thicknesses of both the inner and outer retina (p < 0.001)., Conclusions: Changes in the status of ELM, the presence of DRIL, and the thicknesses of the foveal and parafoveal regions can act as OCT biomarkers, signifying prolonged visual improvements in eyes that have experienced resolved DME after undergoing anti-VEGF therapy., Competing Interests: Declarations of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: This study aimed to analyze the effect of optical coherence tomography angiography (OCTA) on diabetic macular edema (DME) staging and assess the efficacy of laser photocoagulation., Methods: Eighty-six patients (141 eyes) with suspected DME who visited our hospital from August 2019 to March 2022 were selected and underwent fundus angiography and OCTA. The two examination methods were compared in terms of their efficacy in macular edema staging. Subsequently, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of OCTA in diagnosing DME were assessed using fundus angiography as the gold standard. In patients with clinically significant macular edema (CSME) treated with laser photocoagulation, the central concave non-perfused zone (FAZ), vascular density (VD), central macular retinal thickness (CRT), whole retinal blood flow density (FD-300), superficial capillary plexus (SCP), and deep capillary plexus (DCP) were measured using the OCTA 3 mm × 3 mm mode before treatment, at 3 months after treatment, and at 6 months after treatment. SCP, deep capillary plexus (DCP), blood flow density (VD), best corrected visual acuity (BCVA), and central retinal thickness (CRT) were recorded before treatment, 3 months after treatment, and 6 months after treatment. The correlation between BCVA and pre-treatment OCTA parameters at 6 months after treatment was analyzed using Pearson's correlation., Results: Fundus angiography was performed in 86 patients (141 eyes) with suspected DME. Of the 141 eyes, 44 had no leakage, 52 had diffuse edema, 40 had focal macular edema, and 5 had eyes ischemia. A total of 97 eyes showed CSME on fundus angiography. Using fundus angiography as the gold standard, OCTA exhibited a sensitivity of 97.94 %, a specificity of 63.64 %, and an accuracy of 87.23 % in diagnosing CSME. The Kappa value between OCTA and fundus angiography was 0.674. The receiver operating characteristic curve revealed that the area under the curve (AUC) of OCTA in diagnosing CSME was 0.808 (95 % confidence interval: 0.717-0.899). The BCVA was higher, while the CRT was lower in CSME patients at 3 and 6 months after treatment (P<0.05). No significant difference was observed in the OCTA parameters in CSME patients at 3 months after treatment compared with that before treatment (P>0.05). Similarly, no significant difference was found in the FD300 of CSME patients at 6 months after treatment compared with that before treatment (P>0.05). However, the FAZ area, DCP-VD (overall, central concave, and paracentral concave), and SCP-VD (overall, central concave, and paracentral concave) were higher in CSME patients at 6 months after treatment compared with that before treatment (P<0.05). Pearson's correlation showed that BCVA was positively correlated with pre-treatment FAZ area, DCP-VD, and SCP-VD (r>0, P<0.05), and negatively associated with CRT (r<0, P<0.05)., Conclusion: OCTA exhibited high sensitivity and specificity in diagnosis and staging DME. It adeptly captures the microvascular and visual changes in the central macular recess before and after laser photocoagulation therapy, which can quantitatively guide the follow-up treatment of DME., Competing Interests: Declaration of competing interest The authors declared no conflict of interest existing in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: Declaration of competing interest The authors of this study declare no conflict of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jing, Sun, Cheng, Li and Wang.)

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., (© 2024 IOP Publishing Ltd.)

Purpose: This study aimed to determine the changes that occur in the vasculature, as based on optical coherence tomography angiography (OCTA) after non-damaging endpoint management (EpM), using a continuous wave yellow laser. The study was on eyes with diabetic macular edema (DME) that were resistant to anti-vascular endothelial growth factors (anti-VEGFs)., Materials and Methods: This was a retrospective analysis of OCTA images of 44 eyes in 44 patients with DME refractory to anti-VEGF. The eyes were treated with a PASCAL Streamline yellow laser (577 nm wavelength, 200 mm spot size). Treatment was administered to the DME area and utilized 10% continuous wave laser energy and 0.50 µm beam diameter spot spacing. Best-corrected visual acuity (BCVA) and enhanced in-depth imaging with optical coherence tomography (EDI-OCT) and fundus autofluorescence (FAF) images were recorded at baseline, and 3 and 6 months posttreatment. Total choroidal area (TCA), luminal area (LA), stromal area (SA), and the choroidal vascularity index (CVI) were calculated using Image J software. The macula was divided into five quadrants in accordance with the mapping system in the Early Treatment Diabetic Retinopathy Study (ETDRS)., Results: All patients (mean age: 58.90 ± 9.55 years) were diagnosed with diabetes mellitus type 2. Mean BCVA at baseline was 0.30 ± 0.11 logarithm of the minimum angle of resolution (logMAR) versus 0.23 ± 0.10 logMAR at 3 months (p = 0.032) and 0.17 ± 0.10 logMAR at 6 months (p = 0.013). The foveal avascular zone area (FAZ) decreased in the deep capillary plexus (DCP) from baseline to 6 months (p = 0.028). Vessel densities (VDs) of the superficial capillary plexus (SCP), DCP, and choriocapillaris decreased significantly in the fovea at 3 and 6 months compared to baseline (p < 0.05 for both follow-up time points). There were significant decreases in SCP and DCP in the superior quadrant at the end of month 6 (p = 0.001 and p = 0.038, respectively). There was a significant decrease in the nasal quadrant of the DCP and choriocapillaris at the end of month 6 (p = 0.024 and p = 0.049, respectively). Although there was a significant decrease in central macular thickness (CMT) (p < 0.001), subfoveal choroidal thickness (SFCT) (p < 0.001), and LA (p = 0.034) at months 3 and 6, there was no significant change in the CVI (p = 0.19). According to the DME recovery rate, 36 eyes (81%) were irradiated once, whereas 8 eyes (19%) were irradiated twice., Conclusions: Non-damaging EpM therapy using a continuous wave yellow laser in eyes with DME that are resistant to anti-VEGFs induces significant changes in the SCP, choriocapillaris, and, most commonly, the DCP, which caused a significant decrease in VDs during 6 months of follow-up., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

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., Competing Interests: Disclosures A.A. Fawzi is a consultant at Regeneron, Roche/Genentech, Boehringer Ingelheim, RegenXbio, 3Helix, and Optos, Inc, and reports grant support from Boehringer Ingelheim. The other authors report no conflicts.

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., (© 2024 by the American Diabetes Association.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lawali, Wu, Adam, Lin, Kong, Yi, Fang, Niu, Tang, Amza, Zhang, Yu, Yuan and Hu.)

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., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

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., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Alterations in ocular blood flow play an important role in the pathogenesis of diabetic macular edema; however, this remains unclear., Objectives: This study aimed to investigate ocular blood flow in eyes with or without diabetic macular edema using arterial spin labeling., Methods: This cross-sectional study included 118 eyes of 65 patients with diabetic retinopathy analyzed between November 2018 and December 2019. We included a total of 53 eyes without diabetic macular edema (mean [SD] age, 57.83 [7.23] years; 29 men [54.7%]) and 65 eyes with diabetic macular edema (mean [SD] age, 60.11 [7.63] years; 38 men [58.5%]). Using a 3.0-T magnetic resonance imaging, participants were imaged with arterial spin labeling with multiple post-labeling delays., Results: The mean ocular blood flow at post-labeling delays of 1.5 and 2.5 s was significantly lower in eyes with diabetic macular edema among patients with diabetic retinopathy compared with the remaining subgroups (P=0.022 and P <0.001, respectively). The mean ocular blood flow exhibited a significant decrease in eyes with diabetic macular edema when the post-labeling delay was set at 2.5 s in the nonproliferative and proliferative diabetic retinopathy groups, compared with the remaining subgroups (P=0.005 and P=0.002, respectively). The cutoff points of ocular blood flow at post-labeling delays of 1.5 s and 2.5 s were 9.40 and 11.10 mL/100 g/min, respectively., Conclusion: Three-dimensional pseudocontinuous arterial spin labeling can identify differences in the ocular blood flow of patients with diabetic eyes with and without diabetic macular edema., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s).)