Optical Coherence Tomography Volumes Research Articles

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection

Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early intervention and facilitating effective disease management to mitigate further vision deterioration. The advent of optical coherence tomography (OCT) has marked a transformative era in ophthalmology, offering detailed visualization of the macula and optic nerve head (ONH) regions. In recent years, both 2D and 3D convolutional neural network (CNN) algorithms have been applied to OCT image analysis. While 2D CNNs rely on post-prediction aggregation of all B-scans within OCT volumes, 3D CNNs allow for direct glaucoma prediction from the OCT data. However, in the absence of extensively pre-trained 3D models, the comparative efficacy of 2D and 3D-CNN algorithms in detecting glaucoma from volumetric OCT images remains unclear. Therefore, this study explores the efficacy of glaucoma detection through volumetric OCT images using select state-of-the-art (SOTA) 2D-CNN models, 3D adaptations of these 2D-CNN models with specific weight transfer techniques, and a custom 5-layer 3D-CNN-Encoder algorithm. The performance across two distinct datasets is evaluated, each focusing on the macula and the ONH, to provide a comprehensive understanding of the models’ capabilities in identifying glaucoma. Our findings demonstrate that the 2D-CNN algorithm consistently provided robust results compared to their 3D counterparts tested in this study for glaucoma detection, achieving AUC values of 0.960 and 0.943 for the macular and ONH OCT test images, respectively. Given the scarcity of pre-trained 3D models trained on extensive datasets, this comparative analysis underscores the overall utility of 2D and 3D-CNN algorithms in advancing glaucoma diagnostic systems in ophthalmology and highlights the potential of 2D algorithms for volumetric OCT image-based glaucoma detection.

A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study.

Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans. The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model. The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R2 = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R2 = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis. The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema.

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). 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. 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. 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.

Outer Retinal Thickness Is Associated With Cognitive Function in Normal Aging to Intermediate Age-Related Macular Degeneration.

Research on Alzheimer's disease (AD) and precursor states demonstrates a thinner retinal nerve fiber layer (NFL) compared to age-similar controls. Because AD and age-related macular degeneration (AMD) both impact older adults and share risk factors, we asked if retinal layer thicknesses, including NFL, are associated with cognition in AMD. Adults ≥ 70 years with normal retinal aging, early AMD, or intermediate AMD per Age-Related Eye Disease Study (AREDS) nine-step grading of color fundus photography were enrolled in a cross-sectional study. Optical coherence tomography (OCT) volumes underwent 11-line segmentation and adjustments by a trained operator. Evaluated thicknesses reflect the vertical organization of retinal neurons and two vascular watersheds: NFL, ganglion cell layer-inner plexiform layer complex (GCL-IPL), inner retina, outer retina (including retinal pigment epithelium-Bruch's membrane), and total retina. Thicknesses were area weighted to achieve mean thickness across the 6-mm-diameter Early Treatment of Diabetic Retinopathy Study (ETDRS) grid. Cognitive status was assessed by the National Institutes of Health Toolbox cognitive battery for fluid and crystallized cognition. Correlations estimated associations between cognition and thicknesses, adjusting for age. Based on 63 subjects (21 per group), thinning of the outer retina was significantly correlated with lower cognition scores (P < 0.05). No other retinal thickness variables were associated with cognition. Only the outer retina (photoreceptors, supporting glia, retinal pigment epithelium, Bruch's membrane) is associated with cognition in aging to intermediate AMD; NFL was not associated with cognition, contrary to AD-associated condition reports. Early and intermediate AMD constitute a retinal disease whose earliest, primary impact is in the outer retina. Our findings hint at a unique impact on the brain from the outer retina in persons with AMD.

Three-dimensional particle streak velocimetry based on optical coherence tomography for assessing preimplantation embryo movement in mouse oviduct in vivo.

The mammalian oviduct (or fallopian tube) is a tubular organ hosting reproductive events leading to pregnancy. Dynamic 3D imaging of the mouse oviduct with optical coherence tomography (OCT) has recently emerged as a promising approach to study the hidden processes vital to elucidate the role of oviduct in mammalian reproduction and reproductive disorders. In particular, with an intravital window, in vivo OCT imaging is a powerful solution to studying how the oviduct transports preimplantation embryos towards the uterus for pregnancy, a long-standing question that is critical for uncovering the functional cause of tubal ectopic pregnancy. However, simultaneously tracking embryo movement and acquiring large-field-of-view images of oviduct activity in 3D has been challenging due to the generally limited volumetric imaging rate of OCT. A lack of OCT-based 3D velocimetry method for large, sparse particles acts as a technical hurdle for analyzing the mechanistic process of the embryo transport. Here, we report a new particle streak velocimetry method to address this hurdle. The method relies on the 3D streak of a moving particle formed during the acquisition of a single OCT volume, where double B-scans are acquired at each B-scan location to resolve ambiguity in assessing the movement of particle. We validated this method with the gold-standard, direct volumetric particle tracking in a flow phantom, and we demonstrated its in vivo applications for simultaneous velocimetry of embryos and imaging of oviduct. This work sets the stage for quantitative understanding of the oviduct transport function in vivo, and the method fills in a gap in OCT-based velocimetry, providing the potential to enable new applications in 3D flow imaging.

Validation of a deep learning model for automatic detection and quantification of five OCT critical retinal features associated with neovascular age-related macular degeneration

PurposeTo develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular degeneration (nAMD).Methods300 optical coherence tomography volumes from subject eyes with...

En Face and Volumetric Comparison of Hypertransmission Defects Evaluated by Cirrus and Spectralis Optical Coherence Tomography

PurposeTo evaluate and compare en face and 3-dimensional (D) properties of hypertransmission defects (HTDs) between different optical coherence tomography (OCT) devices using OCT volumes and reconstructed en face images. SettingsComparative diagnostic evaluation study. MethodsThirty eyes with dry age-related macular degeneration (AMD) that underwent dense OCT macular volume scans with both the Spectralis (97 B-scans/volume; 2,910 B-scans in total) and Cirrus OCT (128 B-scans/volume; 3,840 B-scans in total) from AMISH EYE STUDY cohort were included in this analysis. HTD regions were labeled on each B-scan and reconstructed into en face and 3-D volume images. Properties of HTD volume were compared between the two devices. ResultsQualitative score of en face images for HTD was higher for the Cirrus compared to the Spectralis (P < 0.01). The quality of Spectralis en face images improved after preprocessing and reconstruction. The 2-D HTD area on en face obtained from 2-D projections of 3-D volume did not differ between devices (P = 0.478; ICC 0.998; Jaccard index 0.721 ± 0.086). There was no difference in the number, volume, PALs, and surface areas of HTDs between devices in the volumetric analysis (all P ≥ 0.090). The signal intensity of HTD normalized by the mean choroidal signal intensity did not differ between devices (P = 0.861). ConclusionsThe visualization of HTD on en face images from Spectralis OCT could be enhanced through image processing. The equivalence in 3-D HTD parameters between the two devices suggests interchangeability for assessing these lesions in AMD.

Denoising in optical coherence tomography volumes for improved 3D visualization.

Optical coherence tomography (OCT) has already become one of the most important diagnostic tools in different fields of medicine, as well as in various industrial applications. The most important characteristic of OCT is its high resolution, both in depth and the transverse direction. Together with the information on the tissue density, OCT offers highly precise information on tissue geometry. However, the detectability of small and low-intensity features in OCT scans is limited by the presence of speckle noise. In this paper we present a new volumetric method for noise removal in OCT volumes, which aims at improving the quality of rendered 3D volumes. In order to remove noise uniformly, while preserving important details, the proposed algorithm simultaneously observes the estimated amounts of noise and the sharpness measure, and iteratively enhances the volume until it reaches the required quality. We evaluate the proposed method using four quality measures as well as visually, by evaluating the visualization of OCT volumes on an auto-stereoscopic 3D screen. The results show that the proposed method outperforms reference methods both visually and in terms of objective measures.

Analysis of OCT Scanning Parameters in AMD and RVO.

Optical coherence tomography (OCT) is an extensively used imaging tool for disease monitoring in both age-related macular degeneration (AMD) and retinal vein occlusion (RVO). However, there is limited literature on minimum requirements of OCT settings for reliable biomarker detection. This study systematically investigates both the influence of scan size and interscan distance (ISD) on disease activity detection. We analyzed 80 OCT volumes of AMD patients and 12 OCT volumes of RVO patients for the presence of subretinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelium detachment (PED). All volume scans had a scan size of 6 × 6 mm and an ISD of 125 µm. We analyzed both general fluid distribution and how biomarker detection sensitivity decreases when reducing scan size or density. We found that in AMD patients, all fluids were nearly normally distributed, with most occurrences in the foveal center and concentric decrease towards the periphery. When reducing the scan size to 3 × 3 and 2 × 2 mm, disease activity detection was still high (0.98 and 0.96). Increasing ISD only slightly can already compromise biomarker detection sensitivity (0.9 for 250 µm ISD against 125 µm ISD).

The Dresden in vivo OCT dataset for automatic middle ear segmentation

Endoscopic optical coherence tomography (OCT) offers a non-invasive approach to perform the morphological and functional assessment of the middle ear in vivo. However, interpreting such OCT images is challenging and time-consuming due to the shadowing of preceding structures. Deep neural networks have emerged as a promising tool to enhance this process in multiple aspects, including segmentation, classification, and registration. Nevertheless, the scarcity of annotated datasets of OCT middle ear images poses a significant hurdle to the performance of neural networks. We introduce the Dresden in vivo OCT Dataset of the Middle Ear (DIOME) featuring 43 OCT volumes from both healthy and pathological middle ears of 29 subjects. DIOME provides semantic segmentations of five crucial anatomical structures (tympanic membrane, malleus, incus, stapes and promontory), and sparse landmarks delineating the salient features of the structures. The availability of these data facilitates the training and evaluation of algorithms regarding various analysis tasks with middle ear OCT images, e.g. diagnostics.

Risk factors for development of hyper-reflective foci overlying drusen in eyes with intermediate age-related macular degeneration

AimsThe aim of this study is to assess baseline characteristics of drusen preceding the development of intraretinal hyper-reflective foci (IHRF) in eyes with intermediate age-related macular degeneration (AMD).MethodsIn this retrospective...

Rigid alignment method for secondary analyses of optical coherence tomography volumes.

Optical coherence tomography (OCT) provides micron level resolution of retinal tissue and is widely used in ophthalmology. Millions of pre-existing OCT images are available from research and clinical databases. Analysis of this data often requires or can benefit significantly from image registration and reduction of speckle noise. One method of reducing noise is to align and average multiple OCT scans together. We propose to use surface feature information and whole volume information to create a novel and simple pipeline that can rigidly align, and average multiple previously acquired 3D OCT volumes from a commercially available OCT device. This pipeline significantly improves both image quality and visualization of clinically relevant image features over single, unaligned volumes from the commercial scanner.

Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.

We wanted to develop a deep-learning algorithm to automatically segment optic nerve head (ONH) and macula structures in three-dimensional (3D) wide-field optical coherence tomography (OCT) scans and to assess whether 3D ONH or macula structures (or a combination of both) provide the best diagnostic power for glaucoma. A cross-sectional comparative study was performed using 319 OCT scans of glaucoma eyes and 298 scans of nonglaucoma eyes. Scans were compensated to improve deep-tissue visibility. We developed a deep-learning algorithm to automatically label major tissue structures, trained with 270 manually annotated B-scans. The performance was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D-CNN) was then designed using 500 OCT volumes and corresponding automatically segmented labels. This algorithm was trained and tested on three datasets: cropped scans of macular tissues, those of ONH tissues, and wide-field scans. The classification performance for each dataset was reported using the area under the curve (AUC). Our segmentation algorithm achieved a DC of 0.94 ± 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field scans, followed by ONH scans, and finally macula scans, with AUCs of 0.99 ± 0.01, 0.93 ± 0.06 and 0.91 ± 0.11, respectively. This study showed that wide-field OCT may allow for significantly improved glaucoma diagnosis over typical OCTs of the ONH or macula. This could lead to mainstream clinical adoption of 3D wide-field OCT scan technology.

Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7MHz wide field swept source FDML optical coherence tomography.

The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid. We present a single segmentation framework technique for segmenting the entire layers present in the chorio-retinal complex of the human eye using optical coherence tomography (OCT) images. This automatic program is based on the graph theory method. This single program is capable of segmenting seven layers of the retina and choroid scleral interface. The graph theory was utilized to find the probability matrix and subsequent boundaries of different layers. The program was also implemented to segment angiographic maps of different chorio-retinal layers using "segmentation matrices." The method was tested and successfully validated on OCT images from six normal human eyes as well as eyes with non-exudative age-related macular degeneration (AMD). The thickness of microstructure and microvasculature for different layers located in the chorio-retinal segment of the eye was also generated and compared. A decent efficiency in terms of processing time, sensitivity, and accuracy was observed compared to the manual segmentation and other existing methods. The proposed method automatically segments whole OCT images of chorio-retinal complex with augmented probability maps generation in OCT volume dataset. We have also evaluated the segmentation results using quantitative metrics such as Dice coefficient and Hausdorff distance This method realizes a mean descent Dice similarity coefficient (DSC) value of 0.82 (range, 0.816-0.864) for RPE and CSI layer.

Evaluation of machine learning algorithms using OCT and OCT angiography for the diagnosis of multiple sclerosis

Aims/Purpose: We present a comprehensive evaluation of machine learning (ML) algorithms using Optical coherence tomography (OCT) and optical coherence tomography angiography (OCT‐A) for the diagnosis of Multiple sclerosis (MS).Methods: Prospective observational study using OCT volumes acquired with Cirrus HD‐OCT 5000 (Carl Zeiss, Meditec, Dublin, CA, EEUU) using the scanning protocols Optic Disc Cube 200 × 200 and Macular Cube 512 × 128, and OCT‐A macular volumes 175 × 350 with FOV 6 × 6 mm and OCT‐A of the Optic Nerve Head (ONH) with FOV 4.5 × 4.5 mm. The total data set consisted of 79 eyes of 40 patients with MS and 54 eyes of 27 control subjects. We evaluated classification algorithms such as Support Vector Machines, k‐Nearest Neighbour, Decision Trees, Random Forest, Extra Trees Classifier and Gaussian NB using data extracted from Ganglion Cell Analysis (8 parameters), Macular Thickness Analysis (7 parameters) and Optic Disc Analysis (5 parameters) from the OCT volumes and 5 parameters from the analysis of the macular OCT‐A volumes and 6 parameters from the analysis of the ONH. We studied all the ML algorithms considering three scenarios: OCT (20 parameters), OCT‐A (11 parameters), and OCT + OCT‐A (31 parameters). The models were trained using a 5‐fold cross‐validation strategy. We calculated the accuracy, F1‐score for each model and the area under the curve (AUC) was used to select the best ML algorithm. We applied SHAP (SHapley Additive exPlanations) to explain the output of the best ML model.Results: For the MS diagnosis, the best results were obtained for OCT with Gaussian NB (accuracy 0.865 ± 0.085, F1‐score 0.864 ± 0.086 and AUC 0.871 ± 0.081), for OCT‐A with Random Forest (accuracy 0.832 ± 0.032, F1‐score 0.832 ± 0.043 and AUC 0.831 ± 0.043) and for OCT + OCT‐A with Gaussian NB (accuracy 0.885 ± 0.028, F1‐score 0.886 ± 0.028 and AUC 0.891 ± 0.034). The input parameters with the highest contribution to the model's predictions were the minimum ganglion cell thickness and the temporal flow index in the ETDRS grid.Conclusions: This study highlights the efficacy of machine learning techniques in utilizing combined parameters from OCT and OCT‐A images of the macula and ONH tests to facilitate early diagnosis of MS. Moreover, it underscores the significance of ophthalmic examination in the comprehensive management of the disease.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

Greater reduction in hyperreflective foci with faricimab compared to aflibercept in patients with diabetic macular oedema

Aims/Purpose: In diabetic macular oedema (DME), hyperreflective foci (HRF) may be a biomarker for disease severity and progression. This post hoc analysis aimed to test if faricimab, a dual‐action therapeutic inhibiting both vascular endothelial growth factor (VEGF)‐A and angiopoietin‐2 (Ang‐2), yields improved resolution of retinal HRF in DME compared to aflibercept.Methods: Phase 3 YOSEMITE/RHINE (NCT03622580/NCT03622593) spectral‐domain optical coherence tomography (SD‐OCT) volumes from 1527 DME patients were automatically segmented using a deep learning‐based HRF model trained with manually‐annotated B‐scans from phase 2 BOULEVARD (NCT02699450). Bright objects ≤50 μm in diameter were classified as retinal HRF. HRF volumes were assessed within the 1‐ and 3‐mm diameter Early Treatment Diabetic Retinopathy Study (ETDRS) rings, separated into inner and outer retina. Treatment groups were compared using a Mixed Model for Repeated Measures.Results: In the inner retina at 1 mm diameter, adjusted mean HRF volumes reduced more markedly from baseline to week 48 for faricimab every 8 weeks (Q8W; −118.29 pL) and faricimab treat&amp;extend (T&amp;E; −130.05 pL) than aflibercept Q8W (−58.67 pL; p = 0.0006 and p &lt; 0.0001, for comparison of each faricimab arm vs aflibercept, averaged across all visits, respectively). In the inner retina at 3‐mm diameter, reductions from baseline to week 48 were − 406.76 pL (faricimab Q8W), −533.01 pL (faricimab T&amp;E) and − 397.67 pL (aflibercept Q8W; p = 0.0142 and p = 0.0034, for comparison to aflibercept averaged across all visits, respectively). Similar, statistically significant results were obtained for the outer retina.Conclusions: Significantly greater retinal HRF volume reductions were observed with faricimab compared to aflibercept. Our findings of better resolution of the pathological manifestations of DME with faricimab support the therapeutic potential of dual Ang‐2/VEGF‐A inhibition.

Exploring Vitreous Haze as a Potential Biomarker for Accelerated Glymphatic Outflow and Neurodegeneration in Multiple Sclerosis: A Cross-Sectional Study.

The glymphatic system removes neurodegenerative debris. The ocular glymphatic outflow is from the eye to the proximal optic nerve. In multiple sclerosis (MS), atrophy of the optic nerve increases the glymphatic outflow space. Here, we tested whether vitreous haze (VH) can provide novel insights into the relationship between neurodegeneration and the ocular glymphatic system in MS. This cross-sectional study comprised 315 persons with MS and 87 healthy controls (HCs). VH was quantified from optical coherence tomography (OCT) volume scans. Neurodegeneration was determined on three-dimensional T1 (3DT1) MRI, lesion detection on fluid-attenuated inversion (FLAIR), and layer thickness on OCT. Generalized estimating equations, corrected for age, were used to analyze associations between VH and metrics for neurodegeneration, demographics, and clinical scales. Group differences were determined between mild, moderate, and severe disability. On the group level, VH scores were comparable between MS and control (p = 0.629). In MS, VH scores declined with disease duration (β = -0.009, p = 0.004) and age (β = -0.007, p = 0.001). There was no relation between VH scores and higher age in HCs. In MS patients, VH was related to normalized gray (NGMV, β = 0.001, p = 0.011) and white matter volume (NWMV, β = 0.001, p = 0.003), macular ganglion cell-inner plexiform layer thickness (mGCIPL, β = 0.006, p < 0.001), and peripapillary retinal nerve fiber layer thickness (pRNFL, β = 0.004, p = 0.008). VH was significantly lower in severe compared to mild disability (mean difference -28.86%, p = 0.058). There is a correlation between VH on OCT and disease duration, more severe disability and lower brain volumes in MS. Biologically, these relationships suggest accelerated glymphatic clearance with disease-related atrophy.