Acute macular neuroretinopathy following Valsalva manoeuvre: an insight into the pathophysiology.

Acute macular neuroretinopathy (AMN) affects the outer retina and is most likely induced by non-inflammatory ischaemia of the retinal deep capillary plexus and choriocapillaris. A man in his early 20s developed Valsalva retinopathy following weightlifting at the gym and presented with blurring of vision in the left eye 1 month after the initial retinal haemorrhages had resolved. A diffuse, purplish, donut-shaped, perifoveal lesion was seen on funduscopy and was well defined by an optical coherence tomography angiography (OCTA) en face image in the left eye. Outer retinal changes on optical coherence tomography (OCT) and a dense co-localised scotoma on a visual field (VF) examination confirmed the diagnosis of AMN, and the patient was started on a tapering dose of oral steroids. Improvement was seen in OCT, OCTA and VF during the 6-month follow-up visit. The use of OCTA en face imaging enabled the accurate identification of the lesion in the affected layers of the retina.

Association between retinal microvascular abnormalities and late-life brain amyloid-ß deposition: the ARIC-PET study.

BACKGROUND: Retinal microvascular signs are accessible measures of early alterations in microvascular dysregulation and have been associated with dementia; it is unclear if they are associated with AD (Alzheimer's disease) pathogenesis as a potential mechanistic link. This study aimed to test the association of retinal microvascular abnormalities in mid and late life and late life cerebral amyloid. METHODS: Participants from the ARIC-PET (Atherosclerosis Risk in Communities-Positron Emission Tomography) study with a valid retinal measure (N = 285) were included. The associations of mid- and late-life retinal signs with late-life amyloid-ß (Aß) by florbetapir PET were tested. Two different measures of Aß burden were included: (1) elevated amyloid (SUVR > 1.2) and (2) continuous amyloid SUVR. The retinal measures' association with Aß burden was assessed using logistic and robust linear regression models. A newly created retinal score, incorporating multiple markers of retinal abnormalities, was also evaluated in association with greater Aß burden. RESULTS: Retinopathy in midlife (OR (95% CI) = 0.36 (0.08, 1.40)) was not significantly associated with elevated amyloid burden. In late life, retinopathy was associated with increased continuous amyloid standardized value uptake ratio (SUVR) (ß (95%CI) = 0.16 (0.02, 0.32)) but not elevated amyloid burden (OR (95%CI) = 2.37 (0.66, 9.88)) when accounting for demographic, genetic and clinical risk factors. A high retinal score in late life, indicating a higher burden of retinal abnormalities, was also significantly associated with increased continuous amyloid SUVR (ß (95% CI) = 0.16 (0.04, 0.32)) independent of vascular risk factors. CONCLUSIONS: Retinopathy in late life may be an easily obtainable marker to help evaluate the mechanistic vascular pathway between retinal measures and dementia, perhaps acting via AD pathogenesis. Well-powered future studies with a greater number of retinal features and other microvascular signs are needed to test these findings.

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.

Atypical multiple evanescent white dot syndrome presenting with peripheral retinal lesions.

Multiple evanescent white dot syndrome (MEWDS) is a rare inflammatory eye condition. We report an atypical case of MEWDS in a man in his 30s who presented with blurred vision (visual acuity 6/9), floaters and photopsia in his left eye. Funduscopy examination showed mild peripheral nasal vascular sheathing with subtle grey-white dots highlighted on fundus autofluorescence. As far as the authors are aware, this is the first case presentation whereby areas affected by MEWDS started in the peripheral retina and migrated centrally. Fluorescein angiography showed hyperfluorescent areas in wreath-like patterns nasally. Optical coherence tomography showed disruption of the ellipsoid zone and hyperreflective projections into the outer nuclear layer. The size of the involved area increased over 3 weeks and subsequently resolved over 4 months. Simultaneously, the patient's symptoms also resolved, without treatment. This case highlights the importance of multimodal imaging, especially ultrawidefield imaging in diagnosing MEWDS.

The clinical evaluation of a widefield lens to expand the field of view in optical coherence tomography (OCT-A).

This study aimed to evaluate the clinical benefits of incorporating a widefield lens (WFL) in optical coherence tomography angiography (OCT-A) in patients with retinal vascular diseases in comparison to standard single-shot OCT-A scans. Sixty patients with retinal vascular diseases including diabetic retinopathy (DR) and retinal vein occlusion (RVO) were recruited. OCT-A imaging (PlexElite 9000) with and without WFL was performed in randomized order. The assessment included patient comfort, time, field of view (FoV), image quality and pathology detection. Statistical analysis included paired t-tests, Mann-Whitney U-tests and Bonferroni correction for multiple tests, with inter-grader agreement using the kappa coefficient. Using a WFL did not lead to statistically significant differences in DR and RVO group test times. Patient comfort remained high, with similar responses for WFL and non-WFL measurements. The WFL notably expanded the scan field (1.6× FoV increase), enhancing peripheral retinal visibility. However, image quality varied due to pathology and eye dominance, affecting the detection of peripheral issues in RVO and DR cases. The use of a WFL widens the scan field, aiding vascular retinal disease imaging with minor effects on comfort, time, and image quality. Further enhancements are needed for broader view angles, enabling improved quantification of non-perfused areas and more reliable peripheral proliferation detection.

Novel optical coherence tomography findings in a patient with methanol toxicity: a case report.

We report a case of acute methanol toxicity with unique optical coherence tomography findings. A 56-year-old man was referred to our ophthalmology clinic with a history of handmade vodka consumption and vision loss. On ophthalmologic examination, his vision was 20/100 in his right eye and 20/200 in his left eye. Bilateral mild optic disk hyperemia was detected on fundus examination. Because of the severity of systemic symptoms in such cases, it is very difficult to include optical coherence tomography in the ophthalmologic examination. However, we managed to perform optical coherence tomography and recorded shallow subretinal fluid and a prominent middle limiting membrane sign as acute retinal structural changes in the patient. The patient was treated with hemodialysis, intravenous ethanol, and sodium bicarbonate. On the fourth day of treatment, visual acuity improved to 20/20 in both eyes. In addition, the prominent middle limiting membrane sign and subretinal fluid disappeared. In this unusual case, retinal pigment epithelium damage and retinal ischemia may have contributed to the prominent middle limiting membrane and subretinal fluid, which are novel optical coherence tomography findings of methanol toxicity.

Retinal imaging for the assessment of stroke risk: a systematic review.

BACKGROUND: Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk. METHODS: A biomedical literature search was performed to identify prospective studies that assess the role of retinal imaging derived biomarkers as indicators of stroke risk. RESULTS: Twenty-four studies were included in this systematic review. The available evidence suggests that wider retinal venules, lower fractal dimension, increased arteriolar tortuosity, presence of retinopathy, and presence of retinal emboli are associated with increased likelihood of stroke. There is weaker evidence to suggest that narrower arterioles and the presence of individual retinopathy traits such as microaneurysms and arteriovenous nicking indicate increased stroke risk. Our review identified three models utilizing artificial intelligence algorithms for the analysis of retinal images to predict stroke. Two of these focused on fundus photographs, whilst one also utilized optical coherence tomography (OCT) technology images. The constructed models performed similarly to conventional risk scores but did not significantly exceed their performance. Only two studies identified in this review used OCT imaging, despite the higher dimensionality of this data. CONCLUSION: Whilst there is strong evidence that retinal imaging features can be used to indicate stroke risk, there is currently no predictive model which significantly outperforms conventional risk scores. To develop clinically useful tools, future research should focus on utilization of deep learning algorithms, validation in external cohorts, and analysis of OCT images.

Retro-mode imaging in acute posterior multifocal placoid pigment epitheliopathy.

AIM: to provide a detailed description and multimodal imaging (MMI) including retro-mode imaging of acute posterior multifocal placoid pigment epitheliopathy (APMPPE). METHODS: Case report of a young male patient presenting with APMPPE picture. Initially, visual acuity testing was performed, followed by biomicroscopic and fundus examinations along with MMI including Optical Coherence Tomography (OCT), fundus autofluorescence (FAF), fluorescein angiography (FA), Indocyanine Green (ICG) angiography, and Retro-mode imaging. The patient was then monitored over a duration of two months. RESULTS: visual acuity was 20/20 with normal biomicroscopic examination; fundus examination detected multiple pale placoid lesions. MMI was consistent with typical APMPPE. Notably, Retro-mode imaging revealed numerous crater-like round lesions that corresponded to those observed on angiography. CONCLUSION: Retromode imaging in APMPPE can serve as a non-invasive tool that highlights the number and distribution of lesions as well as on angiography.

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

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

Recent advances in the use of optical coherence tomography in neuro-ophthalmology: A review.

Optical coherence tomography (OCT) is an in vivo imaging modality that provides non-invasive, high resolution and fast cross-sectional images of the optic nerve head, retina and choroid. OCT angiography (OCTA) is an emerging tool. It is a non-invasive, dye-free imaging approach of visualising the microvasculature of the retina and choroid by employing motion contrast imaging for blood flow detection and is gradually receiving attention for its potential roles in various neuro-ophthalmic and retinal conditions. We will review the clinical utility of the OCT in the management of various common neuro-ophthalmic and neurological disorders. We also review some of the OCTA research findings in these conditions. Finally, we will discuss the limitations of OCT as well as introduce other emerging technologies.

Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.

BACKGROUND: Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical settings, as well as its reasoning, have not been explored in depth. OBJECTIVE: This study aimed to evaluate ChatGPT's diagnostic performance and inference abilities for retinal vascular diseases in a non-English clinical environment. METHODS: In this cross-sectional study, we collected 1226 fundus fluorescein angiography reports and corresponding diagnoses written in Chinese and tested ChatGPT with 4 prompting strategies (direct diagnosis or diagnosis with a step-by-step reasoning process and in Chinese or English). RESULTS: Compared with ChatGPT using Chinese prompts for direct diagnosis that achieved an F1-score of 70.47%, ChatGPT using English prompts for direct diagnosis achieved the best diagnostic performance (80.05%), which was inferior to ophthalmologists (89.35%) but close to ophthalmologist interns (82.69%). As for its inference abilities, although ChatGPT can derive a reasoning process with a low error rate (0.4 per report) for both Chinese and English prompts, ophthalmologists identified that the latter brought more reasoning steps with less incompleteness (44.31%), misinformation (1.96%), and hallucinations (0.59%) (all P<.001). Also, analysis of the robustness of ChatGPT with different language prompts indicated significant differences in the recall (P=.03) and F1-score (P=.04) between Chinese and English prompts. In short, when prompted in English, ChatGPT exhibited enhanced diagnostic and inference capabilities for retinal vascular disease classification based on Chinese fundus fluorescein angiography reports. CONCLUSIONS: ChatGPT can serve as a helpful medical assistant to provide diagnosis in non-English clinical environments, but there are still performance gaps, language disparities, and errors compared to professionals, which demonstrate the potential limitations and the need to continually explore more robust large language models in ophthalmology practice.

Optical coherence tomography in children with inherited retinal disease.

Recent advances have led to therapeutic options becoming available for people with inherited retinal disease. In particular, gene therapy has been shown to hold great promise for slowing vision loss from inherited retinal disease. Recent studies suggest that gene therapy is likely to be most effective when implemented early in the disease process, making consideration of paediatric populations important. It is therefore necessary to have a comprehensive understanding of retinal imaging in children with inherited retinal diseases, in order to monitor disease progression and to determine which early retinal biomarkers may be used as outcome measures in future clinical trials. In addition, as many optometrists will review children with an inherited retinal disease, an understanding of the expected imaging outcomes can improve clinical care. This review focuses on the most common imaging modality used in research assessment of paediatric inherited retinal diseases: optical coherence tomography. Optical coherence tomography findings can be used in both the clinical and research setting. In particular, the review discusses current knowledge of optical coherence tomography findings in eight paediatric inherited retinal diseases - Stargardt disease, Bests disease, Leber's congenital amaurosis, choroideremia, RPGR related retinitis pigmentosa, Usher syndrome, X-linked retinoschisis and, Batten disease.

Alignment, calibration, and validation of an adaptive optics scanning laser ophthalmoscope for high-resolution human foveal imaging.

In prior art, advances in adaptive optics scanning laser ophthalmoscope (AOSLO) technology have enabled cones in the human fovea to be resolved in healthy eyes with normal vision and low to moderate refractive errors, providing new insight into human foveal anatomy, visual perception, and retinal degenerative diseases. These high-resolution ophthalmoscopes require careful alignment of each optical subsystem to ensure diffraction-limited imaging performance, which is necessary for resolving the smallest foveal cones. This paper presents a systematic and rigorous methodology for building, aligning, calibrating, and testing an AOSLO designed for imaging the cone mosaic of the central fovea in humans with cellular resolution. This methodology uses a two-stage alignment procedure and thorough system testing to achieve diffraction-limited performance. Results from retinal imaging of healthy human subjects under 30 years of age with refractive errors of less than 3.5 diopters using either 680 nm or 840 nm light show that the system can resolve cones at the very center of the fovea, the region where the cones are smallest and most densely packed.

Predicting systemic diseases in fundus images: systematic review of setting, reporting, bias, and models' clinical availability in deep learning studies.

BACKGROUND: Analyzing fundus images with deep learning techniques is promising for screening systematic diseases. However, the quality of the rapidly increasing number of studies was variable and lacked systematic evaluation. OBJECTIVE: To systematically review all the articles that aimed to predict systemic parameters and conditions using fundus image and deep learning, assessing their performance, and providing suggestions that would enable translation into clinical practice. METHODS: Two major electronic databases (MEDLINE and EMBASE) were searched until August 22, 2023, with keywords 'deep learning' and 'fundus'. Studies using deep learning and fundus images to predict systematic parameters were included, and assessed in four aspects: study characteristics, transparent reporting, risk of bias, and clinical availability. Transparent reporting was assessed by the TRIPOD statement, while the risk of bias was assessed by PROBAST. RESULTS: 4969 articles were identified through systematic research. Thirty-one articles were included in the review. A variety of vascular and non-vascular diseases can be predicted by fundus images, including diabetes and related diseases (19%), sex (22%) and age (19%). Most of the studies focused on developed countries. The models' reporting was insufficient in determining sample size and missing data treatment according to the TRIPOD. Full access to datasets and code was also under-reported. 1/31(3.2%) study was classified as having a low risk of bias overall, whereas 30/31(96.8%) were classified as having a high risk of bias according to the PROBAST. 5/31(16.1%) of studies used prospective external validation cohorts. Only two (6.4%) described the study's calibration. The number of publications by year increased significantly from 2018 to 2023. However, only two models (6.5%) were applied to the device, and no model has been applied in clinical. CONCLUSION: Deep learning fundus images have shown great potential in predicting systematic conditions in clinical situations. Further work needs to be done to improve the methodology and clinical application.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Non-vasogenic cystoid maculopathy in autosomal recessive bestrophinopathy: novel insights from NIR-FAF and OCTA imaging.

BACKGROUND: Autosomal Recessive Bestrophinopathy (ARB) is an inherited retinal disease caused by biallelic mutations in the BEST1 gene. Herein, we report the multimodal imaging findings of ARB presenting with cystoid maculopathy and investigate the short-term response to combined systemic and topical carbonic anhydrase inhibitors (CAIs). MATERIAL AND METHODS: An observational, prospective, case series on two siblings affected by ARB is presented. Patients underwent genetic testing and optical coherence tomography (OCT), blue-light fundus autofluorescence (BL-FAF), near-infrared fundus autofluorescence (NIR-FAF), fluorescein angiography (FA), MultiColor imaging, and OCT angiography (OCTA). RESULTS: Two male siblings, aged 22 and 16, affected by ARB resulting from c.598C>T, p.(Arg200*) and c.728C>A, p.(Ala243Glu) BEST1 compound heterozygous variants, presented with bilateral multifocal yellowish pigment deposits scattered through the posterior pole that corresponded to hyperautofluorescent deposits on BL-FAF. Vice versa, NIR-FAF mainly disclosed wide hypoautofluorescent areas in the macula. A cystoid maculopathy and shallow subretinal fluid were evident on structural OCT, albeit without evidence of dye leakage or pooling on FA. OCTA demonstrated disruption of the choriocapillaris throughout the posterior pole and sparing of intraretinal capillary plexuses. Six months of combined therapy with oral acetazolamide and topical brinzolamide resulted in limited clinical benefit. CONCLUSIONS: We reported two siblings affected by ARB, presenting as non-vasogenic cystoid maculopathy. Prominent alteration of NIR-FAF signal and concomitant choriocapillaris rarefaction on OCTA were noted in the macula. The limited short-term response to combined systemic and topical CAIs might be explained by the impairment of the RPE-CC complex.

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm.

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

Hierarchical Knowledge Guided Learning for Real-World Retinal Disease Recognition.

In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited number of samples), which results in a challenging long-tailed learning scenario. Some recently published datasets in ophthalmology AI consist of more than 40 kinds of retinal diseases with complex abnormalities and variable morbidity. Nevertheless, more than 30 conditions are rarely seen in global patient cohorts. From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training. In addition, there may be more than one disease for the presence of the retina, resulting in a challenging label co-occurrence scenario, also known as multi-label, which can cause problems when some re-sampling strategies are applied during training. To address the above two major challenges, this paper presents a novel method that enables the deep neural network to learn from a long-tailed fundus database for various retinal disease recognition. Firstly, we exploit the prior knowledge in ophthalmology to improve the feature representation using a hierarchy-aware pre-training. Secondly, we adopt an instance-wise class-balanced sampling strategy to address the label co-occurrence issue under the long-tailed medical dataset scenario. Thirdly, we introduce a novel hybrid knowledge distillation to train a less biased representation and classifier. We conducted extensive experiments on four databases, including two public datasets and two in-house databases with more than one million fundus images. The experimental results demonstrate the superiority of our proposed methods with recognition accuracy outperforming the state-of-the-art competitors, especially for these rare diseases.