Retinal abnormalities in a patient with cutis marmorata telangiectatica congenita.

Cutis marmorata telangiectatica congenita is a rare congenital vascular malformation characterised by cutaneous vascular abnormalities, typically diagnosed at birth or in the early postnatal period. Although typically benign, this disease is associated with other systemic abnormalities, including rare ocular alterations, such as congenital glaucoma, cataracts and retinopathy.This manuscript describes a female infant, who presented with generalised livedo reticularis, a band of alopecia and cutaneous atrophy in the temporal region above the coronal suture. The patient was diagnosed with cutis marmorata telangiectatica congenita by a paediatrician, and an ophthalmological evaluation was requested. A funduscopy examination in both eyes showed temporal and superior retina with avascular areas with new vessels, venous dilations and shunts, and no retinal detachments. Given these findings, we performed retinal photocoagulation laser treatment with excellent results.This case report highlights the importance of early ophthalmological evaluation of children with this disease to prevent secondary complications, such as vitreous haemorrhage and tractional retinal detachment.

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

Significance: 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. Aim: 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. Approach: 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). Results: 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. Conclusions: 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.

Optical coherence elastography under homolateral parallel acoustic radiation force excitation for ocular elasticity quantification.

Alteration in the elastic properties of biological tissues may indicate changes in the structure and components. Acoustic radiation force optical coherence elastography (ARF-OCE) can assess the elastic properties of the ocular tissues non-invasively. However, coupling the ultrasound beam and the optical beam remains challenging. In this Letter, we proposed an OCE method incorporating homolateral parallel ARF excitation for measuring the elasticity of the ocular tissues. An acoustic-optic coupling unit was established to reflect the ultrasound beam while transmitting the light beam. The ARF excited the ocular tissue in the direction parallel to the light beam from the same side of the light beam. We demonstrated the method on the agar phantoms, the porcine cornea, and the porcine retina. The results show that the ARF-OCE method can measure the elasticity of the cornea and the retina, resulting in higher detection sensitivity and a more extensive scanning range.

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

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.

Observation of structural and vascular features of retina and choroid in myopia using ultra-widefield SS-OCTA.

BACKGROUND: To find the relationship between the changes of retinal and choriodal structure/ vascular densities (VD) and the myopia progress. METHODS: 126 eyes of 126 age-matched young participants were divided into three groups: Emmetropia and Low Myopia (EaLM) (33 eyes), Moderate Myopia (MM) (39 eyes), and High Myopia (HM) (54 eyes). Fundus images measuring 12 × 12 mm were captured using ultra-widefield swept-source optical coherence tomography angiography (SS-OCTA). Each image was uniformly divided into nine regions: supra-temporal (ST), temporal (T), infra-temporal (IT), superior (S), central macular area (C), inferior (I), supra-nasal (SN), nasal (N), and infra-nasal (IN). Various structural parameters, including inner retina thickness (IRT), outer retina thickness (ORT), and choroid thickness (CT), were assessed, and the VD of the superficial capillary plexus (SCP), deep capillary plexus (DCP), choriocapillaries (CC), and choroid vessels (ChdV) were quantified. RESULTS: CT in upper fundus exhibited a significant reduction from EaLM to MM. Additionally, ORT (ST, S. SN, C, N, IT, I, IN), CT (ST, S, SN, T, C, N, IT, I, IN) and VDs of SCP (ST, S, C, I, IN), DCP (ST, S, T, C, I) and ChdV (T, N, I, IN) were statistically diminished in EaLM compared to HM. Furthermore, IRT (N), ORT (N, IN), CT (S, SN, T, C, IT, I) and VDs of SCP (I, IN) and DCP (I) exhibited significant decreases as MM progressed towards HM. Intriguingly, there was a notable increase in the VD of CC (ST, S, T, C, N) as myopia progressed from MM to HM. CONCLUSION: Significant changes in retinal and choroid structure and vascular density occur as moderate myopia advances to high myopia. Efforts to curb myopia progression to this stage are essential, as the failure to do so may lead to the development of corresponding retinopathy.

Quantifying Putative Retinal Gliosis in Preclinical Alzheimer's Disease.

Purpose: Neuroinflammation plays a significant role in the pathology of Alzheimer's disease (AD). Mouse models of AD and postmortem biopsy of patients with AD reveal retinal glial activation comparable to central nervous system immunoreactivity. We hypothesized that the surface area of putative retinal gliosis observed in vivo using en face optical coherence tomography (OCT) imaging will be larger in patients with preclinical AD versus controls. Methods: The Spectralis II instrument was used to acquire macular centered 20 × 20 and 30 × 25-degrees spectral domain OCT images of 76 participants (132 eyes). A cohort of 22 patients with preclinical AD (40 eyes, mean age = 69 years, range = 60-80 years) and 20 control participants (32 eyes, mean age = 66 years, range = 58-82 years, P = 0.11) were included for the assessment of difference in surface area of putative retinal gliosis and retinal nerve fiber layer (RNFL) thickness. The surface area of putative retinal gliosis and RNFL thickness for the nine sectors of the Early Treatment Diabetic Retinopathy Study (ETDRS) map were compared between groups using generalized linear mixed models. Results: The surface area of putative retinal gliosis was significantly greater in the preclinical AD group (0.97 ± 0.55 mm2) compared to controls (0.68 ± 0.40 mm2); F(1,70) = 4.41, P = 0.039; Cohen's d = 0.61. There was no significant difference between groups for RNFL thickness in the 9 ETDRS sectors, P > 0.05. Conclusions: Our analysis shows greater putative retinal gliosis in preclinical AD compared to controls. This demonstrates putative retinal gliosis as a potential biomarker for AD-related neuroinflammation.

Spiral scanning improves subject fixation in widefield retinal imaging.

Point scanning retinal imaging modalities, including confocal scanning light ophthalmoscopy (cSLO) and optical coherence tomography, suffer from fixational motion artifacts. Fixation targets, though effective at reducing eye motion, are infeasible in some applications (e.g., handheld devices) due to their bulk and complexity. Here, we report on a cSLO device that scans the retina in a spiral pattern under pseudo-visible illumination, thus collecting image data while simultaneously projecting, into the subject's vision, the image of a bullseye, which acts as a virtual fixation target. An imaging study of 14 young adult volunteers was conducted to compare the fixational performance of this technique to that of raster scanning, with and without a discrete inline fixation target. Image registration was used to quantify subject eye motion; a strip-wise registration method was used for raster scans, and a novel, to the best of our knowledge, ring-based method was used for spiral scans. Results indicate a statistically significant reduction in eye motion by the use of spiral scanning as compared to raster scanning without a fixation target.

Influence of static and dynamic ocular aberrations on full-field optical coherence tomography for in vivo high-resolution retinal imaging.

Under spatially incoherent illumination, time-domain full-field optical coherence tomography (FFOCT) offers the possibility to achieve in vivo retinal imaging at cellular resolution over a wide field of view. Such performance is possible, albeit there is the presence of ocular aberrations even without the use of classical adaptive optics. While the effect of aberrations in FFOCT has been debated these past years, mostly on low-order and static aberrations, we present, for the first time to our knowledge, a method enabling a quantitative study of the effect of statistically representative static and dynamic ocular aberrations on FFOCT image metrics, such as SNR, resolution, and image similarity. While we show that ocular aberrations can decrease FFOCT SNR and resolution by up to 14 dB and fivefold, we take advantage of such quantification to discuss different possible compromises between performance gain and adaptive optics complexity and speed, to optimize both sensor-based and sensorless FFOCT high-resolution retinal imaging.

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

[Multifocal electroretinography in the diagnosis and monitoring of early and intermediate stages of age-related macular degeneration]. / Mul'tifokal'naya elektroretinografiya v diagnostike i monitoringe rannei i promezhutochnoi stadii vozrastnoi makulyarnoi degeneratsii.

Multifocal electroretinography is a valuable diagnostic method for the objective localization and quantitative assessment of functional disorders of the central retina in age-related macular degeneration. It is used to detect early changes, monitor the course of the disease and treatment outcomes. In many cases, multifocal electroretinography is a more sensitive method for detecting functional disorders at the early/intermediate stage of age-related macular degeneration compared to morphological (optical coherence tomography) and subjective (visual acuity, perimetry) testing methods.

Impact of lens autofluorescence and opacification on retinal imaging.

BACKGROUND: Retinal imaging, including fundus autofluorescence (FAF), strongly depends on the clearness of the optical media. Lens status is crucial since the ageing lens has both light-blocking and autofluorescence (AF) properties that distort image analysis. Here, we report both lens opacification and AF metrics and the effect on automated image quality assessment. METHODS: 227 subjects (range: 19-89 years old) received quantitative AF of the lens (LQAF), Scheimpflug, anterior chamber optical coherence tomography as well as blue/green FAF (BAF/GAF), and infrared (IR) imaging. LQAF values, the Pentacam Nucleus Staging score and the relative lens reflectivity were extracted to estimate lens opacification. Mean opinion scores of FAF and IR image quality were compiled by medical readers. A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations. RESULTS: Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). Predictions were comparable to human grading. Overall, LQAF showed the highest correlation with image quality grading criteria for all imaging modalities (eg, Pearson correlation±CI -0.35 (-0.50 to 0.18) for BAF/LQAF). BAF image quality was most vulnerable to an increase in lenticular metrics, while IR (-0.19 (-0.38 to 0.01)) demonstrated the highest resilience. CONCLUSION: The use of CNN-based retinal image quality assessment achieved excellent results. The study highlights the vulnerability of BAF to lenticular remodelling. These results can aid in the development of cut-off values for clinical studies, ensuring reliable data collection for the monitoring of retinal diseases.

Electroretinography in congenital nystagmus patients with a normal fundus examination.

PURPOSE: To identify the ophthalmic causes of congenital nystagmus with normal eye examination by electroretinography (ERG). STUDY DESIGN: Retrospective observational study. METHODS: We reviewed the medical records of patients younger than 6 months of age who presented between June 2008 and November 2011 with nystagmus and no other neurological signs following an otherwise normal eye examination. A complete ophthalmic examination and ERG (Nicolet Bravo system; Nicolet Biomedial & RETIscan; Roland Instruments), fundus photography, and Ishihara color test were performed to identify any ophthalmic causes of congenital nystagmus. RESULTS: Thirty-three patients met the criteria. Rod dysfunction was diagnosed in 4 patients (12.1%), cone dysfunction in 2 patients (6.1%), and cone-rod dysfunction in 1 patient (3.0%). The results of ERG were negative in 2 patients (6.1%). Idiopathic infantile nystagmus was diagnosed in the remaining 24 patients (72.7%) based on their normal ERG examination. CONCLUSIONS: In Korean congenital nystagmus patients with a normal fundus examination, achromatopsia and Leber's congenital amaurosis are uncommon causes. ERG is needed to make a definite diagnosis and provide prognostic information in congenital idiopathic nystagmus patients with a normal fundus examination.

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

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

Functional OCT reveals anisotropic changes of retinal flicker-evoked vasodilation.

The purpose of this study is to verify the effect of anisotropic property of retinal biomechanics on vasodilation measurement. A custom-built optical coherence tomography (OCT) was used for time-lapse imaging of flicker stimulation-evoked vessel lumen changes in mouse retinas. A comparative analysis revealed significantly larger (18.21%) lumen dilation in the axial direction compared to the lateral (10.77%) direction. The axial lumen dilation predominantly resulted from the top vessel wall movement toward the vitreous direction, whereas the bottom vessel wall remained stable. This observation indicates that the traditional vasodilation measurement in the lateral direction may result in an underestimated value.

FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation.

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation.

Evaluation of Increase in Retinal Thickness as Diagnostic Marker in Central Artery Occlusion.

PURPOSE: To evaluate the increase in retinal thickness as a marker in predicting the onset of central retinal artery occlusions. METHODS: Retrospective clinical study conducted at one Swiss hospital. Electronic records were filtered for patients with artery occlusions. Optical coherence tomography data, including time between the imaging and ischemic event, were reviewed. Increase in relative retinal thickness was measured, defined as an increase in retinal thickness compared to the unaffected partner eye. This was correlated with the time from symptom onset. A cutoff value of relative increase of < 24.5% was applied, as suggested in previous studies. The results were compared to the time gathered from the electronic records, and sensitivity, specificity, positive predictive value as well as negative predictive value were calculated for predicting an ischemia time of < 4.5 h. RESULTS: Forty-two eyes from 41 patients with central artery occlusions were identified. Fourteen were female. Mean age was 66.4 ± 15.8 years. Initial corrected visual acuity was 2.41 ± 0.68 logMAR, and 2.13 ± 0.87 logMAR at the last follow-up (p > 0.05). Of eyes with a visual acuity of counting fingers (n = 38) or worse, 89.5% showed no improvement during follow-up, while eyes with logMAR 1 or better (n = 4) improved. Thirteen eyes (13 patients) presented within 4.5 h of the ischemic event. Four patients received i. v. thrombolysis, with visual recovery in one. In 12 eyes with an ischemia time of < 4.5 h, relative increase was below 24.5%. In the remaining 29 eyes with > 4.5 h, relative increase was below 24.5% in 4 eyes and above 24.5% in 25 eyes. This yielded a sensitivity of 92.3%, a specificity of 86.2%, with a positive predictive value of 75.0% and a negative predictive value of 96.2%. CONCLUSION: Central retinal artery occlusion is associated with severe vision loss. There is no current established therapy. Parameters that objectify the presence of a therapeutic window for thrombolysis are gaining in importance as patient history is often imprecise. Relative retinal thickness increase proved a noninvasive imaging parameter demonstrating adequate performance in detecting patients within the therapeutic window of thrombolysis. Further investigation of this parameter in central retinal occlusion is warranted.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

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_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_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_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.