Search Results (311)

Background/Objectives: The utility of optical coherence tomography angiography (OCTA) for neovascular age-related macular degeneration (nAMD) remains unclear. The current study investigated the choroidal neovascularization (CNV) detection rate by OCTA in comparison with standard fluorescein angiography (FA) and spectral-domain optical coherence tomography (SD-OCT). Methods: Subjects underwent multimodal imaging, including FA, SD-OCT, and OCTA imaging, which were compared. In patients with unilateral nAMD, the contralateral eye with dry AMD (n = 39) was included to determine imaging modality sensitivity and specificity. Eyes with inaccurate automated segmentation from retinal distortion were manually resegmented. Results: The diagnostic performance for nAMD was 86% sensitivity and 100% specificity by OCT (AUC: 0.93; 95% CI 0.87–0.99; p < 0.001); 82% sensitivity and 100% specificity by FA (AUC: 0.91; 95% CI 0.84–0.98; p < 0.001); and 68% sensitivity and 100% specificity by automatically segmented OCTA (AUC: 0.84; 95% CI 0.76–0.93; p < 0.001). OCTA diagnostic accuracy improved following manual resegmentation to 88% sensitivity and 100% specificity (AUC: 0.94; 95% CI 0.89–1.0; p < 0.001). Diagnostic accuracy of OCT combined with manually resegmented OCTA (AUC: 1.0; 95% CI 1.0–1.0; p < 0.001) was greater than that of OCT or FA combined (AUC: 0.96; 95% CI 0.92–1.0; p < 0.001) but both were very accurate. Conclusions: Manual segmentation of the OCTA images can help identify CNV in eyes otherwise undetected by automated segmentation algorithms due to errors in segmentation of retinal layers. Eyes with substantial elevation in one or more layers of the retina were most likely to benefit from resegmentation. Full article

Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular network can provide additional quantitative biomarkers. This study develops and validates a binary classification method for distinguishing healthy from glaucomatous fundus images by combining deep-learning-based vessel segmentation, fractal and multifractal analysis, and textural features. The public ORIGA dataset is utilized. Images are converted to grayscale using three alternative approaches, followed by Gray-Level Co-occurrence Matrix texture analysis and fractal analysis based on the differential box-counting method. Vessel segmentation is implemented via a U-Net neural network trained on a combination of public datasets, after which multifractal analysis is performed on the resulting binary masks. The extracted features are used to train and compare several machine learning models with hyperparameter optimization. The best-performing model among ONH-based features (Random Forest) achieves 75.00%; however, a logistic regression model using multifractal parameters and CDR reaches 86.17%, substantially outperforming the CDR-only baseline (66.15%). Notably, while classical fractal dimension shows only marginal differences (1–2% relative change) between groups, multifractal parameters reveal distinct changes: the multifractal spectrum width $\Delta \alpha$ increases markedly and the minimum singularity exponent ${\alpha }_{\min }$ decreases in glaucomatous eyes, indicating increased heterogeneity of the vascular network. These findings suggest that multifractal characteristics of the vascular network can serve as reliable and sensitive biomarkers for automated glaucoma screening, offering clear advantages over classical fractal analysis. Full article

Macular holes are uncommon in pediatric patients and are most often associated with ocular trauma. Idiopathic cases are rare and may present as subtle clinical findings and atypical imaging features. We report a case of a 13-year-old boy presenting with decreased visual acuity in the left eye. Best-corrected visual acuity was 1.0 in the right eye and 0.5 in the left eye, with unremarkable anterior segment examination. Optical coherence tomography showed a foveal defect characterized by a central hyporeflective cavity with disruption of retinal layers, without evidence of a full-thickness defect. Fluorescein angiography demonstrated central hyperfluorescence without leakage. Color fundus photography revealed a subtle central foveal lesion, while electrophysiological testing and visual field examination were within normal limits. This case highlights that early structural abnormalities of the fovea in pediatric patients may present with minimal clinical findings and preserved retinal function. Multimodal imaging, particularly OCT, plays a key role in detecting subtle foveal alterations and may aid in identifying early stages within the spectrum of macular hole formation. Careful monitoring is warranted due to the potential for progression. Full article

Ocular ductions deform the optic disc and peripapillary blood vessels, and deformations can be interpreted as mechanical strain. We used confocal scanning laser ophthalmoscopy (cSLO) to map strain in disc and peripapillary retinal vessels associated with the cardiac pulse and determine if such strain is influenced by gaze direction. Sets of 13 infrared cSLO images were obtained sequentially for each eye using a Heidelberg Spectralis scanner in cinematic mode over a 3 sec interval in adults. Imaging was repeated in central, and horizontally (30° adduction/abduction) and vertically eccentric gazes (10° supraduction/infraduction). Retinal vessels, optic disc, and fovea were segmented using custom-trained, deep learning-based models. Frame to frame vascular displacements were automatically determined using optical flow analysis, allowing computation of equivalent strain. A total of 25 eyes of 13 subjects of mean age 39 ± 18 (standard deviation, range: 25 to 81) years were included. Average equivalent strain over 3 sec ranging from 0.27% to 0.36% exceeded the 0.16% noise threshold across all gazes and regions, indicating measurable pulse-induced deformation. After adjustment for age and axial length, pulsatile maximum and minimum strain were influenced slightly by gaze direction, maximally for supraduction, whereas mean strain did not vary significantly with gaze. The cardiac pulse induces measurable deformation of retinal vessels that can be quantified as equivalent strain in the image plane using optical flow-derived displacement fields. However, the interaction of pulse strain with gaze direction is unlikely to be a significant confound for investigations of strains associated with eye movements. Full article

Objective: This study aimed to evaluate the predictability of baseline optical coherence tomography angiography (OCTA) metrics utilizing a specialized capillary–large vessel segmentation analysis framework in patients with diabetic macular edema (DME) undergoing anti-vascular endothelial growth factor (anti-VEGF) therapy. Methods: Forty-two treatment-naïve eyes with DME receiving three monthly loading anti-VEGF injections were included. Superficial capillary plexus (SCP) images from 3 × 3 mm OCTA scans were processed to isolate the capillary network from the large vessels via image processing. Vessel density and skeleton density were extracted for the total, large-vessel, and capillary components. Multiple linear and logistic regression models were used to identify independent predictors of post-treatment best-corrected visual acuity (BCVA) and “good visual outcome” (≥3-line improvement or final BCVA of 20/40 or better). Results: Following three monthly anti-VEGF injections, the mean BCVA significantly improved from 0.57 ± 0.36 to 0.37 ± 0.30 LogMAR (p < 0.0001), and the mean central retinal thickness decreased from 424.3 ± 117.7 μm to 316.9 ± 84.7 μm (p < 0.0001). The proportion of patients who achieved a good visual outcome was 73.8%. Baseline central retinal thickness was associated with baseline BCVA (p = 0.049) but not predictive of post-treatment BCVA (p = 0.38) or good visual outcomes (p = 0.79). Baseline capillary vessel density was identified as a significant independent predictor of post-treatment BCVA (p = 0.024), whereas total and large-vessel metrics were not. Capillary vessel density was also the only significant predictor of good visual outcomes (p = 0.044). Conclusions: Baseline capillary vessel density is a robust predictor of visual prognosis after anti-VEGF therapy in patients with DME, underscoring the importance of capillary network integrity in functional recovery. Full article

Retinal vessel segmentation remains challenging for thin vessels and low-contrast bifurcations. We evaluate a Swin-UNet-style model family that conditions decoder features with a single-channel local fractal dimension prior refined by a short learnable anisotropic diffusion model and injected through Spatially-Adaptive Normalization (SPADE). On Fundus Image Vessel Segmentations (FIVES), the strongest no-test-time-augmentation result was obtained by OPT-I v2 at 200 epochs, reaching Dice 0.8899, clDice 0.8517, and Area Under the ROC Curve (AUC) 0.9904, compared with 0.8643, 0.8125, and 0.9856 for the matched 200-epoch baseline. In a matched Neural Cellular Automata (NCA)/no-NCA ablation using the same seed, data, 200-epoch budget, and evaluation pipeline, enabling NCA improved the test Dice from 0.8813 to 0.8907 and the test clDice from 0.8325 to 0.8518, with NCA winning on all 80 paired test images for both metrics. The results support PDE (partial differential equation)-SPADE fractal prior conditioning and NCA topology refinement as ablation-grounded improvements over the tested baseline family, while broader matched external validation requires future work. Full article

Background/Objectives: Retinal layer segmentation in optical coherence tomography (OCT) is essential for the diagnosis and monitoring of retinal diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME). Although deep learning methods have achieved strong performance, most rely on dense pixel-wise predictions and often struggle to preserve anatomical consistency, particularly in regions with low contrast or structural deformation. This study aims to address these limitations by introducing a query-based segmentation framework that explicitly models retinal layer structure. Methods: In this paper, we propose the RetiQueryNet architecture that employs encoding of retinal layers in the form of query embeddings with the use of cross attention to interact with pixel level features encoded by a transformer based encoder. The architecture integrates multi-scale features through a compact query-driven decoder with modest additional computational overhead. Normalization and resizing of OCT images preceded their usage as inputs, while the layer labels were converted to multi-class segmentation maps. In the training process, we used loss function with combination of cross entropy loss and Dice loss. Our model performance was compared with multiple state-of-the-art models such as U-Net, DeepLabV3, FPN, MANet and SegFormer, while performance metrics were Dice, IoU and mean surface distance (MSD). Results: RetiQueryNet was able to attain a mean Dice score of 0.934 ± 0.0046 and outperformed all baseline models on the main performance measures. Improvements were particularly evident in challenging retinal layers such as IBRPE and OBRPE, where boundary ambiguity is high. It should be noted that RetiQueryNet had a relatively lower MSD value, meaning that the predicted boundaries were more accurate. Furthermore, visual observations suggest that the approach generated smooth and coherent segmentations. Conclusions: The findings demonstrate that query-based modeling offers a viable approach to pixel-wise segmentation. In particular, by making use of structural priors in the form of learnable queries, RetiQueryNet improves not only segmentation accuracy but also anatomical consistency. Query-based modeling appears to be an exciting area for retinal image segmentation that could potentially be applied to other applications in medical image segmentation. Full article

Background/Objectives: Granulomatous uveitis comprises a clinically heterogeneous group of inflammatory disorders, including ocular sarcoidosis, Vogt–Koyanagi–Harada disease, sympathetic ophthalmia, tuberculosis-associated uveitis, and syphilitic uveitis. Because these entities may share overlapping posterior segment findings, clinical examination alone is often insufficient for differential diagnosis, particularly when choroidal, retinal, or retinal vascular involvement predominates. Methods: This review provides a clinically oriented overview of multimodal imaging in granulomatous uveitis, including optical coherence tomography (OCT), enhanced-depth imaging OCT, swept-source OCT, OCT angiography, fundus autofluorescence, fluorescein angiography, indocyanine green angiography, and ultrawidefield imaging. Results: Emphasis is placed on imaging patterns that help localize the predominant anatomic compartment of inflammation, distinguish major etiologies, identify diagnostic pitfalls, and assess disease activity over time. By integrating current evidence with representative multimodal imaging findings, we propose an anatomic and decision-oriented framework for interpreting granulomatous posterior segment inflammation. Conclusions: Particular attention is given to the distinction between active inflammation and irreversible structural damage, as this distinction may influence treatment escalation or tapering, timing of elective surgery, local corticosteroid therapy, and the need for diagnostic sampling in infectious or masquerade-like presentations. Full article

Background/Objectives: Retinal vein occlusion (RVO) commonly causes vision loss from macular edema (ME). OCT biomarkers (IRF, SRF, HRF, and ELM/EZ disruption) inform prognosis and treatment but are rarely quantified routinely due to time burden and interobserver variability. We aimed to validate a deep-learning algorithm for automated detection and quantification of key OCT biomarkers in RVO-ME versus expert assessment. Methods: In this retrospective single-center study, 93 eyes with RVO-ME imaged with spectral-domain OCT were analyzed. The AI quantified IRF/SRF volumes, ELM/EZ interruption, and HRF counts. Two masked expert clinicians provided reference evaluations. Performance and agreement were assessed using ROC AUC, Cohen’s kappa, intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis. Image-quality metrics (foveal centration and retinal layer segmentation) were recorded. Results: The AI showed high diagnostic performance (AUC: SRF 0.969; ELM 0.871; EZ 0.958) and substantial-to-almost-perfect agreement (kappa: SRF 0.807; ELM 0.788; EZ 0.914). HRF quantification correlated strongly with experts (r = 0.89, p < 0.001), with very good agreement (ICC = 0.87) and minimal bias. Image-quality accuracy exceeded 98% for foveal centration and layer segmentation. Conclusions: This AI software enables reliable, rapid automated assessment of major OCT biomarkers in RVO-ME, supporting streamlined personalized management; prospective studies should confirm longitudinal monitoring and treatment-guidance value. Full article

Objectives: To assess the association of structural biomarkers derived from optical coherence tomography (OCT) and choriocapillaris (CC) flow information with point-wise retinal sensitivity (PWS) measured by microperimetry (MP) in intermediate age-related macular degeneration (iAMD). Methods: Patients with iAMD received imaging with spectral-domain (SD)-OCT (Spectralis, Heidelberg Engineering) and OCT-angiography (OCT-A) (PLEX Elite 9000, ZEISS). In addition, MP examinations in photopic setting (MP-3, NIDEK) and mesopic background illumination (MAIA2, ICare) were performed. The thickness of the ellipsoid-zone (EZ) and the outer nuclear layer (ONL), as well as the volume of drusen and HRF, were segmented using deep-learning (DL)-based approaches. CC flow deficit percentage (FD%) was extracted from OCT-A slabs using a novel binarization method. Semiautomatic co-registration of MP examinations, OCT-A slabs, and OCT volumes was performed. Three exploratory models were calculated using multivariable mixed-effects models: (1) structure–function (SF) using structural OCT biomarkers, (2) flow–function (FF) utilizing OCT-A derived flow information, and (3) structure–flow–function (SFF) incorporating both OCT and OCT-A data. Model performance was evaluated using AIC and BIC criterion. Results: 19 eyes of 19 patients were evaluated, totalling 3297 MP-stimuli, 1873 B-scans, and 19 OCT-A slabs. Mean (SD) age was 76 (7) years, and sensitivity was 26.0 (3.36) dB in the MP-3 and 22.42 (3.64) dB in the MAIA2. Mesopic MAIA2 examinations showed significantly lower PWS values (−3.56 to −3.63 dB; p < 0.001). Drusen and HRF volume decreased PWS (−0.6 [95% CI: −1.04; −0.16] dB/nL; p = 0.007 and −9.56 [95% CI: −12.86; −6.26] dB/nL; p < 0.001), while ONL was positively associated with PWS (0.06 [0.05; 0.07] at an eccentricity of 5.2°; p < 0.001) in the SF model. CC FD% was not significantly associated with PWS in the FF and the SFF model (p > 0.05 in both cases). In the SFF model drusen volume (−1.69 [95% CI: −2.09; −1.29] dB/nL; p < 0.001), EZ (0.04 [95% CI: 0.02; 0.06] dB/µm; p < 0.001), and ONL thickness (0.03 [95% CI: 0.02; 0.04] dB/µm; p < 0.001) were significant predictors for PWS. The SF model exhibited the lowest AIC and BIC indicating best model performance. Conclusions: Structural parameters derived from SD-OCT such as HRF, drusen volume, and outer retinal layer thickness may be more closely associated with PWS, with CC FD% as an OCT-A-derived metric contributing limited additional explanatory benefit in cross-sectional analyses. Full article

Syphilitic panuveitis is a severe and diagnostically highly challenging manifestation of ocular syphilis. Its predominant posterior-segment involvement and its tendency to mimic noninfectious or viral uveitis may delay etiologic recognition and increase the risk of permanent vision loss. Rhegmatogenous retinal detachment (RRD) is a rare but vision-threatening complication that likely reflects advanced, inflammation-induced disruption of the vitreoretinal interface. A narrative literature review was conducted using the PubMed, Scopus, and Web of Science databases (January 2000 to 10 September 2025). Studies addressing the clinical presentation, imaging findings, pathophysiology, and management of syphilitic panuveitis and associated rhegmatogenous retinal detachment were analyzed. Infectious mimickers were also presented, with particular emphasis on West Nile virus (WNV). Evidence was synthesized qualitatively. Posterior uveitis and panuveitis are one of the most common ocular manifestations of syphilis. Posterior segment involvement in ocular syphilis is frequently bilateral, typically presenting with dense vitritis, retinal vasculitis, and optic neuropathy. RRD is a rare presenting complication, most often developing in areas of prior inflammatory retinitis and arising due to retinal necrosis, persistent vitreoretinal traction, and early proliferative vitreoretinopathy, which increases surgical complexity and may limit functional recovery. HIV coinfection often modifies disease severity. In relevant endemic or seasonal settings, WNV-associated ocular inflammation represents an important diagnostic pitfall. Syphilitic panuveitis should be considered early in patients presenting with unexplained posterior uveitis or panuveitis. Routine testing for syphilis and HIV in the uveitic laboratory palette, together with targeted evaluation for infectious mimickers, is essential to reduce diagnostic delay and avoid inappropriate immunosuppression. RRD should be recognized as a marker of advanced, inflammation-induced vitreoretinal interface damage requiring timely antimicrobial therapy and early involvement of vitreoretinal surgery. Full article

Background/Objectives: To evaluate the associations between corneal topographic irregularity, higher-order aberrations (HOAs), and posterior segment structural and microvascular parameters in keratoconus using optical coherence tomography (OCT) and OCT angiography (OCTA). Methods: In this cross-sectional study, 81 eyes with keratoconus and 60 healthy control eyes underwent corneal topography and wavefront analysis, spectral-domain OCT with enhanced depth imaging, and OCTA. Retinal layer thicknesses, choroidal thickness and area metrics, choroidal vascularity index (CVI), and OCTA-derived vascular parameters were analyzed. Associations were assessed using Spearman correlation analysis with false discovery rate (FDR) correction. Results: Compared with controls, keratoconus eyes showed significantly increased corneal curvature, corneal irregularity indices, and HOAs (all p < 0.001). Structural OCT analysis demonstrated preserved inner retinal layers, whereas outer nuclear layer thickness was reduced (p < 0.001) and overall outer retinal layer thickness was increased (p = 0.005). Choroidal thickness and both total and luminal choroidal areas were significantly greater in keratoconus eyes (all p ≤ 0.011), while CVI did not differ between groups (p > 0.05). OCTA revealed reduced superficial capillary plexus vessel density at the whole image and perifoveal regions (all p < 0.001), whereas deep capillary plexus and foveal avascular zone metrics were largely preserved. Correlation analyses identified only weak and inconsistent associations between corneal parameters, HOAs, and posterior segment measurements, none of which remained statistically significant after FDR correction. Conclusions: Despite pronounced anterior segment deformation and optical degradation, posterior segment structural and microvascular alterations in keratoconus are limited and weakly related to corneal disease severity. These findings support a predominantly anterior segment centered pathophysiology of keratoconus and highlight the importance of stringent multiple-comparison control in multimodal imaging studies. Full article

Background: Graft-versus-host disease is the most common complication after allogeneic hematopoietic stem cell transplantation. While ocular graft-versus-host disease typically manifests as dry eye syndrome and anterior segment involvement, posterior segment complications are rare. Previously reported posterior segment complications in graft-versus-host disease have been limited to acute presentations with significant functional visual impairment. Methods: A 41-year-old man developed progressive retinal nerve fiber layer and ganglion cell layer loss four years after allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia. The patient had established chronic graft-versus-host disease with cutaneous involvement and ocular surface disease. Results: Despite preserved visual acuity and visual fields, and only subtle functional involvement on visual evoked potentials, optical coherence tomography revealed significant reduction in retinal nerve fiber layer thickness and ganglion cell layer. Magnetic resonance imaging showed no optic nerve or brain abnormalities. Conclusions: This case describes an uncommon presentation of chronic, subclinical posterior segment involvement in chronic GVHD and suggests that optical coherence tomography may detect progressive structural retinal changes in the absence of clinically evident visual impairment, supporting its potential role in longitudinal monitoring. Full article

Introduction: Although anterior non-infectious uveitis affects the structures of the anterior segment of the eye, (inflammatory) disruption of the hemato–ocular barrier may lead to changes in the structures of the posterior segment of the eye. Objective: To evaluate functional retinal changes using multifocal electroretinography (mfERG) and their relationship with structural optical coherence tomography (OCT) parameters in patients with acute anterior non-infectious uveitis (AANU). Methods: This prospective study included 38 eyes of 19 patients diagnosed with unilateral AANU and age-matched healthy fellow eyes as controls. All subjects underwent comprehensive ophthalmological examination, including best-corrected visual acuity (BCVA), spectral-domain OCT, and mfERG testing at baseline, 3 months, and 6 months. mfERG parameters (amplitude and implicit times) were analyzed alongside central field thickness (CFT), macular volume (MV), and average macular thickness (AMT). Results: Eyes affected by AANU demonstrated a significant reduction in mfERG response amplitude in the central retinal region compared with control eyes, particularly during the acute phase. Although OCT parameters showed partial structural normalization during follow-up, functional recovery was less pronounced in selected retinal regions. Latency values showed minimal variation over time. These findings indicate a potential dissociation between electrophysiological function and structural morphology during disease resolution. Conclusions: Acute anterior uveitis is associated with measurable macular functional impairment detectable by mfERG, even when structural OCT parameters appear relatively stable. These results suggest that inflammatory processes in AAU may extend beyond the anterior segment and transiently affect retinal function. mfERG may therefore serve as a sensitive adjunct tool for detecting and monitoring subclinical macular dysfunction in AANU. Clinical Relevance: Functional retinal impairment may persist despite apparent structural recovery in acute anterior uveitis. Incorporating mfERG into clinical evaluation may improve the detection of subtle macular involvement and enhance understanding of disease dynamics beyond conventional imaging findings. Full article

This study proposes an improved retinal blood vessel segmentation method to enhance the diagnosis of microvascular retinal complications. The proposed method extracts local shape features from retinal images utilizing a fractional Hessian matrix, which models blood vessels as surface structures characterized by ridges and valleys resulting from variations in curvature. The methodology integrates adaptive principal curvature estimation with a new framework leveraging the fractional Hessian matrix with nonsingular and nonlocal kernels. The effectiveness of the suggested method is assessed using publicly accessible datasets, including DRIVE, HRF, STARE, and some real images obtained from a local hospital. The proposed segmentation achieves 96.77% accuracy and 98.82% specificity on the DRIVE database, 96.91% accuracy and 98.69% specificity on STARE, and 95.90% accuracy and 98.36% specificity on the HRF database. Optimal parameters for the fractional order and Gaussian standard deviation were empirically determined by maximizing segmentation accuracy. Our findings show that the proposed approach achieves competitive performance compared to the listed methods, including several deep learning approaches, while maintaining significant computational efficiency. The output of the suggested method can be further utilized with deep learning techniques, which will be applied in the clinical context of diabetic retinopathy and glaucoma to identify abnormalities likely related to disease progression and different stages. Full article