Color Fundus Photography Research Articles

ANATOMICAL AND FUNCTIONAL OUTCOMES OF BEVACIZUMAB TREATMENT IN PEDIATRIC AUTOSOMAL RECESSIVE BESTROPHINOPATHY.

The purpose of this study was to report functional and anatomical outcomes of anti-VEGF treatment in eyes with autosomal recessive bestrophinopathy (ARB) presenting in the first decade of life. The study included case series of four eyes from two siblings with compound heterozygous mutations in the BEST1 gene who were treated with eight monthly intravitreal bevacizumab injections. Response to treatment was analyzed using color fundus photography, fundus autofluorescence, optical coherence tomography, OCT angiography, and microperimetry. Patient 1 (male, age 9 years) had visual acuity of 20/20 in the right eye and 20/50 in the left eye. Patient 2 (female, age 10 years) had visual acuity of 20/25 in the right eye and 20/20 in the left eye. All eyes had multifocal subretinal deposition of lipofuscin and subretinal fluid, and three eyes had choroidal neovascularization (CNV). Lipofuscin material reabsorbed in 2 of 4 eyes, the CNV regressed in 3 of 3 eyes, a bacillary detachment resolved in 1 of 1 eye, but the subretinal fluid did not change. Functional improvement in visual acuity was noted, but MP showed scattered areas of reduced retinal sensitivity. No ocular or systemic side effects were detected. Anti-VEGF treatment of choroidal neovascularization in eyes with ARB resulted in anatomical changes that were only clinically significant in the eye with decreased visual acuity. The hyporeflective subretinal material remained unchanged suggesting a nonexudative cause. These findings provide new insights into the management of ARB, especially in pediatric subjects with CNV.

Month 60 Imaging Findings and Relationship to Treatment Outcomes Following Anti-VEGF Therapy for Macular Edema Due to Central or Hemi-Retinal Vein Occlusion

To evaluate imaging findings from SCORE2 participants through 60 months, to describe the degree of resolution or progression of these variables, and to correlate changes in these imaging findings to treatment outcomes such as visual acuity and the number of treatments administered. SCORE2 participants were followed for up to 60 months. Visual acuity, injection frequency and imaging tests color fundus photography (CFP), optical coherence tomography (OCT), and ultra-widefield fluorescein angiography [UWFA]) were performed throughout this period. Less than 6% of eyes had subretinal fluid at month 60. Disorganization of the retinal inner layers (DRIL) was the most likely finding to persist, present in 96% of eyes at baseline and unchanged at 95% at month 60. For UWFA, at baseline, there was a mean of 5.0% non-perfusion area (95% CI: 3.3%-6.8%) in the NETWORC grid with little change to month 60. For the Early Treatment Diabetic Retinopathy Study (ETDRS) grid, at baseline, there was a mean of 2.3% non-perfusion area (95% CI: 0.7%-3.9%) with little change to month 60. There was no correlation between any of the imaging variables at baseline and change in visual acuity to month 60 or in the number of injections following the variable treatment timeframe (month 12 to month 60). These analyses provide an anatomic explanation for persistent functional deficits many years following initial treatment. Clinical practice patterns should consider evaluation with these imaging tests to help explain persistent functional deficits in many eyes. Additionally, these 8 baseline imaging variables generally should not be relied on to predict visual acuity or intensity of treatment. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk

PurposeWe aim to use Fundus fluorescein angiography (FFA) to label the capillaries on color fundus photographs (CF) and train a deep learning model to quantify retinal capillaries non-invasively from CF and apply it to cardiovascular disease (CVD) risk assessment.Design: cross-sectional study Subjects90,732 pairs of CF-FFA images from 3893 participants. Main Outcome MeasuresArea under the ROC curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio [95% confidence interval (CI)] for Cox regression analysis. MethodWe matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on seven vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events using data from 49,229 participants in the UK Biobank. ResultsOn the FundusCapi dataset, the segmentation performance was AUC = 0.94, accuracy = 0.93, sensitivity = 0.89, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (p < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91[0.84-0.98] and 0.68[0.54-0.86], respectively). ConclusionsUsing paired CF-FFA images, we automated the laborious manual labeling process and enabled non-invasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events.

Colour tone of retinal arterioles imaged with a colour scanning laser ophthalmoscope can be an indicator of systemic arterial stiffness

ObjectiveColour scanning laser ophthalmoscope (cSLO) offers several advantages, including improved image quality and better visualisation of the retinal structures compared with colour fundus photograph (CFP). This study aimed to identify...

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

Acceleration of convolutional neural network based diabetic retinopathy diagnosis system on field programmable gate array

Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.

Congenital Rubella Syndrome-a Case Report with Clinical Manifestation and Management Approach

Congenital rubella syndrome (CRS) is an illness in infant resulting from maternal infection with rubella virus during pregnancy. Patients suffer from some complications included deafness, cataract, heart defects, mental retardation, bone alterations, liver and spleen damage. Purpose of this study is to create attention, early identification and keep special concern in management of such case. A 15 years old girl presented with dimness of vision since childhood. Her mother had a history of fever and skin rash during prenatal period. She had mental retardation, deafness, dumbness, thin stature, microchephaly. Unaided vision fixed and follows in both eyes. Anterior segments showed bilateral microphthalmos, nystagmus, microcornea, aphakia. Intraocular pressure was 40 mm of Hg in each eye. Color fundus photograph showed myopic crescent with tessellated fundus. Echocardiogram showed pulmonary stenosis. Ultrasonography whole abdomen revealed chronic hepatitis. LDH level was 380 units/L. She had hyperthyroidism. Her sensory neural hearing loss was detected by audiometry. She was referred to glaucoma clinic for raised ocular pressure; low vision clinic for visual rehabilitation; medicine specialist for cardiac problem, liver problem and hormonal imbalance. All presenting features revealed that it was a case of congenital rubella syndrome as the girl had deafness, cardiac problem and ocular abnormality which are triad of CRS. Early diagnosis, timely management and proper counseling to patient and guardian will consequently increase life quality of this patient. National awareness should be increased for full coverage of antenatal check up with MMR vaccination in all health care centers of developing countries. J.Natl.Inst.Ophthalmol.2023;6(1): 54-59

Retinal pigment epithelium-Bruch's membrane volume in grading of age-related macular degeneration.

To assess the agreement of optical coherence tomography (OCT) algorithm-based retinal pigment epithelium -Bruch's membrane complex volume (RBV) with fundus photograph-based age-related macular degeneration (AMD) grading. Digital color fundus photographs (CFPs) and spectral domain OCT images were acquired from 96 elderly subjects. CFPs were graded according to Age-Related Eye Disease Study (AREDS) classification. OCT image segmentation and RBV data calculation were done with Orion™ software. Univariate and multivariate analyses were performed to find out whether AMD lesion features associated with higher RBVs. RBV correlated with AMD grading (rs=0.338, P=0.001), the correlation was slightly stronger in early AMD (n=52; rs=0.432, P=0.001). RBV was higher in subjects with early AMD compared with those with no AMD lesions evident in fundus photographs (1.05±0.20 vs 0.96±0.13 mm3, P=0.023). In multivariate analysis higher RBVs were associated significantly with higher total drusen (β=0.388, P=0.027) and pigmentation areas (β=0.319, P=0.020) in fundus photographs, whereas depigmentation area (β=-0.295, P=0.015) associated with lower RBV. RBV correlate with AMD grading status, with a stronger association in patients with moderate, non-late AMD grades. This effect is driven mostly by lesions with drusen or pigmentation. Lesions with depigmentation tend to have lower values. RBV is more comprehensive measurement of the key area of AMD pathogenesis, compared to sole drusen volume analysis. RBV measurements are independent on grader variations and offer a possibility to quantify early and middle grade AMD lesions in a research setting, but may not substitute fundus photograph-based grading in the whole range of AMD spectrum.

Quantitative analysis of optic disc changes in school-age children with ametropia based on artificial intelligence.

To explore changes in the optic disc and peripapillary atrophy (PPA) in school-age children with ametropia using color fundus photography combined with artificial intelligence (AI) technology. Based on the retrospective case-controlled study, 226 eyes of 113 children aged aged 6-12y were enrolled from October 2021 to May 2022. According to the results of spherical equivalent (SE), the children were divided into four groups: low myopia group (66 eyes), moderate myopia group (60 eyes), high myopia group (50 eyes) and emmetropia control group (50 eyes). All subjects underwent un-aided visual acuity, dilated pupil optometry, best-corrected visual acuity (BCVA), intraocular pressure, ocular axis measurement and color fundus photography. The width of PPA, horizontal diameter ratio of PPA to the optic disc and area ratio of PPA to the optic disc were significantly different among the four groups (P<0.05). The width of the nasal and temporal neuroretinal rim, the roundness of the optic disc, the height of PPA, the vertical diameter ratio of PPA to the optic disc, and the average density of PPA in the high myopia group were significantly different compared with the other three groups (P<0.05). There were strong negative correlations between SE and area ratio of PPA to the optic disc (r=-0.812, P<0.001) and strong positive correlation between axial length (AL) and area ratio of PPA to the optic disc (r=0.736, P<0.001). In school-age children with high myopia, the nasal and temporal neuroretinal rims are narrowed and even lost, which have high sensitivity. The area ratio of the PPA to the optic disc could be used as an early predictor of myopia progression, which is of great significance for the development prevention and management of myopia.

A broad study of machine learning and deep learning techniques for diabetic retinopathy based on feature extraction, detection and classification

Diabetic Retinopathy (DR) is a micro vasculardisorder that affects the eyes and is a long term effectofDiabetesmellitus. The likelihood to develop diabetic retinopathy is directly proportional to the age and duration of the diabetes, as well as increase in the level of blood glucose level and fluctuation in blood pressure levels. A person who has diabetes has more probability to develop diabetic retinopathy. The ration of people with diabetes started to increase from 285 million in 2010 and will reach up to 439 million in the year of 2030.Out of the total number of people with Diabetic Retinopathy, approximately one-fourth of the people have vision-threatening disease. Earlier detection and classificationof Diabetic Retinopathy has to be taken much care in order to sustain a patient’s vision. The diabetic Retinopathy may be classified into various stages like Mild non-proliferative retinopathy, Moderate nonproliferative retinopathy, severe nonproliferative Retinopathy and Proliferative diabetic retinopathy. Theproblem associated with the manual detection of diabetic retinopathy is that the processing time is high, effortconsumingandrequiresanophthalmologist to examine the eye retinal fund us images. The manual analysis includes Visual Acuity testing, Tonometry and Pupil dilation. The vision lost due to Diabetic retinopathy is sometimes irreparable. Hence there is a need for earlier detection and treatment to reduce the risk of blindness.Hence there are various automated methods of diabetic retinopathy screening that have made good progress using image classification, pattern recognition, and machine learning. The input to the automated image classification model can be the color fundus photography or optical Coherence tomography images.

Clinical effectiveness of screening for age-related macular degeneration: A systematic review

Introduction Age-related macular degeneration (AMD) is an eye disease that occurs in patients over 50 years old. Early diagnosis enables timely treatment to stabilize disease progression. However, the fact that the disease is asymptomatic in its early stages can delay treatment until it progresses. As such, screening in specific contexts can be an early detection tool to reduce the clinical and social impact of the disease. Objective Assess the effectiveness of screening methods for early detection of AMD in adults aged 50 years or older. Methods A systematic review of comparative observational studies on AMD screening methods in those aged 50 years or older, compared with no screening or any other strategy. A literature search was conducted in the MEDLINE (via PubMed), Embase, Cochrane Library and Lilacs database. Results A total of 5,290 studies were identified, three of which met the inclusion criteria and were selected for the systematic review. A total of 8,733 individuals (16,780 eyes) were included in the analysis. The screening methods assessed were based on optical coherence tomography (OCT) compared with color fundus photography, and OCT and telemedicine testing compared to a standard eye exam. Conclusion The systematized data are limited and only suggest satisfactory performance in early screening of the population at risk of developing AMD. OCT and the telemedicine technique showed promising results in AMD screening. However, methodological problems were identified in the studies selected and the level of evidence was considered low.

Diabetic Retinopathy and Chronic Kidney Disease: Associations and Comorbidities in a Large Diabetic Population - The Tongren Health Care Study.

The aim of the study was to investigate associations between diabetic retinopathy (DR) and chronic kidney disease (CKD) in patients with type 2 diabetes (TD2). The participants of the cross-sectional, community-based Tongren Health Care Study underwent a detailed medical and ophthalmological examination. We defined TD2 by a fasting plasma glucose concentration of ≥7.0 mmol/L or a medical history. CKD was classified as either reduced estimated glomerular filtration rate (eGFR) of &lt;60 mL/min/1.73 mm2 or presence of albuminuria. DR was assessed using color fundus photographs. Out of 62,217 participants of the Tongren Health Care Study, 5,103 (8.2%) patients had TD2. The prevalence of DR was 12.8% (95% CI, 11.8%, 13.7%), CKD was 13.3% (95% CI, 12.4%, 14.3%), and the subtypes of CKD including reduced eGFR and albuminuria was 4.6% (95% CI, 4.2%, 5.1%) and 10.1% (95% CI, 9.3%, 10.9%), respectively. DR was detectable in 21.0% of the patients with CKD, while CKD was present in 20.9% of the DR patients. Higher DR prevalence was associated with higher prevalence of albuminuria and reduced eGFR (both p &lt; 0.05). Factors independently associated with the presence of CKD instead of DR were older age (p &lt; 0.001, OR = 1.05), a higher body mass index (p &lt; 0.001, OR = 1.14), a higher serum concentration of triglycerides (p &lt; 0.001, OR = 1.26), and a lower blood glucose (p &lt; 0.001, OR = 0.93). Having hypertension was additionally associated with the presence of reduced eGFR as compared with DR (p = 0.005, OR = 4.47). TD2 patients of older age and with higher body mass index, hypertension, and dyslipidemia had a higher probability of being affected by CKD rather than DR, while those with a higher blood glucose level were more prone to DR than CKD.

Multimodal Ophthalmic Imaging in Spinocerebellar Ataxia Type 7.

The aim of this case series and narrative literature review is to highlight the importance of multimodal imaging in the ophthalmological examination of patients with spinocerebellar ataxia type 7 and provide a summary of the most relevant imaging techniques. Three patients with SCA7 were included in this case series. A literature review revealed twenty-one publications regarding ocular manifestations of SCA7, and the most relevant aspects are summarized. The role of different imaging techniques in the follow-up of SCA7 patients is analyzed, including color vision testing, corneal endothelial topography, color fundus photography (CFP) and autofluorescence, near infrared reflectance imaging, spectral domain optical coherence tomography (SDOCT), visual field examination, and electrophysiological tests. SDOCT provides a rapid and non-invasive imaging evaluation of disease progression over time. Additional examination including NIR imaging can provide further information on photoreceptor alteration and subtle disruption of the RPE, which are not evident with CFP at an early stage. Electrophysiological tests provide essential results on the state of cone and rod dystrophy, which could be paramount in guiding future genetic therapies. Multimodal imaging is a valuable addition to comprehensive ophthalmological examination in the diagnosis and management of patients with SCA7.

Diagnostic accuracy of community optometrists for age-related macular degeneration using colour fundus photographs: Apilot evaluation.

The accurate diagnosis of age-related macular degeneration (AMD) represents an important step in delaying and preventing vision loss and achieving optimal patient care. Therefore, this pilot study aimed to estimate the diagnostic accuracy of community optometrists for identifying AMD using colour fundus photographs (CFPs) to support sample size calculations for subsequent definitive studies. Five practising community optometrists were invited to classify a total of 1023 CFPs for the (1) presence of AMD, and, if applicable, (2) stage of AMD (early/intermediate/late geographic atrophy/late neovascular AMD). Diagnosis by referral centre clinicians formed the reference standard. Diagnostic accuracy was assessed by the area under the receiver operating characteristic curve (aROC). Sensitivity, specificity, positive and negative predictive values were also calculated. Of the 1023 CFPs included in the study, 226 images were of AMD and 797 images were of other ocular conditions or no abnormal findings. Participating community optometrists had a mean (SD) age of 30.2 (8.9) years, 60.0% (3/5) were female and the mean number of years practising in primary eye care was 5.4 (5.4) years. Community optometrists demonstrated excellent performance for diagnosing AMD, with an aROC of 0.86 (95% CI 0.83 to 0.89), sensitivity of 84.5% (95% CI 79.1 to 89.0) and specificity of 88.0% (95% CI 85.5 to 90.1). The aROC (95% CI) for diagnosing early, intermediate, late geographic atrophy and late neovascular AMD was 0.82 (0.73 to 0.91), 0.76 (0.72 to 0.81), 0.69 (0.49 to 0.90) and 0.55 (0.34 to 0.75), respectively. These results justify the need for an appropriately powered definitive study to assess community clinicians' diagnostic accuracy for AMD.

Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

ABSTRACT A method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard’s colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula.

A deep learning model for generating fundus autofluorescence images from color fundus photography

BackgroundFundus Autofluorescence (FAF) is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium (RPE) associated with various age-related and disease-related changes. The practical uses of FAF are ever-growing. This study aimed to evaluate the effectiveness of a generative deep learning (DL) model in translating color fundus (CF) images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration (AMD). MethodsA generative adversarial network (GAN) model was trained on pairs of CF and FAF images to generate synthetic FAF images. The quality of synthesized FAF images was assessed objectively by common generation metrics. Additionally, the clinical effectiveness of the generated FAF images in AMD classification was evaluated by measuring the area under the curve (AUC), using the LabelMe dataset. ResultsA total of 8410 FAF images from 2586 patients were analyzed. The synthesized FAF images exhibited an impressive objectively assessed quality, achieving a multi-scale structural similarity index (MS-SSIM) of 0.67. When evaluated on the LabelMe dataset, the combination of generated FAF images and CF images resulted in a noteworthy improvement in AMD classification accuracy, with the AUC increasing from 0.931 to 0.968. ConclusionsThis study presents the first attempt to use a generative deep learning model to create authentic and high-quality FAF images from CF images. The incorporation of the translated FAF images on top of CF images improved the accuracy of AMD classification. Overall, this study presents a promising approach to enhance large-scale AMD screening.

CENTRAL RETINAL ARTERY OCCLUSION AS PRESENTATION OF BARTONELLA ENDOCARDITIS.

To describe a rare ocular presentation of a systemic illness and the important lifesaving diagnosis made by a complete eye examination. The patient was evaluated with a comprehensive ophthalmic examination and followed closely in the outpatient setting with optical coherence tomography, fluorescein angiography, and color fundus photos. A 66-year-old man presented with acute vision loss of the left eye. A complete eye examination revealed that he had a central retinal artery occlusion. Systemic workup revealed that he had a mitral valve vegetation and blood cultures grew Bartonella henselae. His kidney biopsy showed membranoproliferative glomerulonephritis, which is often seen with septic emboli. Furthermore, the patient lacked any ocular inflammatory signs. This constellation of findings was diagnostic for a thromboembolic etiology causing his central retinal artery occlusion. At follow-up, the optical coherence tomography demonstrated inner retinal hyperreflectivity and the fluorescein angiogram showed segmented flow and no neovascularization. On follow-up, the patient had a stable examination with light perception vision and completed antibiotic therapy for bartonella endocarditis. The detection of a fatal systemic illness was made promptly with a thorough ocular examination. We highlight the importance of a multidisciplinary approach in making a lifesaving diagnosis.

A fast and fully automated system for glaucoma detection using color fundus photographs

This paper presents a low computationally intensive and memory efficient convolutional neural network (CNN)-based fully automated system for detection of glaucoma, a leading cause of irreversible blindness worldwide. Using color fundus photographs, the system detects glaucoma in two steps. In the first step, the optic disc region is determined relying upon You Only Look Once (YOLO) CNN architecture. In the second step classification of ‘glaucomatous’ and ‘non-glaucomatous’ is performed using MobileNet architecture. A simplified version of the original YOLO net, specific to the context, is also proposed. Extensive experiments are conducted using seven state-of-the-art CNNs with varying computational intensity, namely, MobileNetV2, MobileNetV3, Custom ResNet, InceptionV3, ResNet50, 18-Layer CNN and InceptionResNetV2. A total of 6671 fundus images collected from seven publicly available glaucoma datasets are used for the experiment. The system achieves an accuracy and F1 score of 97.4% and 97.3%, with sensitivity, specificity, and AUC of respectively 97.5%, 97.2%, 99.3%. These findings are comparable with the best reported methods in the literature. With comparable or better performance, the proposed system produces significantly faster decisions and drastically minimizes the resource requirement. For example, the proposed system requires 12 times less memory in comparison to ResNes50, and produces 2 times faster decisions. With significantly less memory efficient and faster processing, the proposed system has the capability to be directly embedded into resource limited devices such as portable fundus cameras.

Angular Sign of Henle Fiber Layer Hyperreflectivity in Purtscher and Purtscher like Retinopathy.

Angular Sign of Henle Fiber Layer Hyperreflectivity (ASHH) is a marker of Henle Fibre layer ischemia. Our aim is to describe ASHH as a novel OCT sign in Purtscher and Purtscher like retinopathy. Here we present 3 Cases, one classical Purtscher Retinopathy and 2 Purtscher like Retinopathy who had ASHH on SD OCT imaging. Modalities like Colour fundus photography, cross-sectional OCT, OCT angiography (OCTA) and fluorescein angiography were used. Presence ASHH in Purtscher Retinopathy and Purtscher like retinopathy points towards a deep capillary plexus insult and has prognostic implications.

Diagnosing Polypoidal Choroidal Vasculopathy using Color Fundus Photography, Optical Coherence Tomography and Optical Coherence Tomography Angiography.

To compare the diagnostic accuracy of differentiating polypoidal choroidal vasculopathy (PCV) from exudative age-related macular degeneration (AMD), using color fundus photography (CFP), optical coherence tomography (OCT), and swept-source optical coherence tomography angiography (SS-OCTA) without using fluorescein angiography/ indocyanine green angiography (FA/ICGA). Treatment-naïve eyes with exudative age-related macular degeneration (AMD) that underwent CFP, OCT, SS-OCTA, and FA/ICGA imaging before treatment were identified. Images of each patient were categorized into 2 sets (Set A: CFP+OCT; Set B: CFP+ SS-OCTA). In Set B, both the en face and cross-sectional B-scans were analyzed. Each set was reviewed by two graders, and it was determined whether the presumed diagnosis was PCV. Sensitivity, specificity, and area under the curve (AUC) for the diagnosis of PCV were assessed for each set by comparing diagnoses that included ICGA. The number of polypoidal lesions in each set was calculated and compared to ICGA. A total of 94 study eyes from 94 patients with AMD were included in the study, of which 66.0% were male, and the mean age was 71.8±9.0 years. The PCV diagnosis rate using ICGA was 45.7%. Sensitivity of sets A, B: 0.88, 0.93; specificity: 0.94, 0.96; positive predictive value: 0.93, 0.95; negative predictive value: 0.91, 0.94, AUC: 0.90 (95% confidence interval [CI]:0.83-0.97), 0.96 (95% CI:0.90-1.00). Set A detected polypoidal lesions 1.28±0.91, Set B detected polypoidal lesions 1.47±1.01, ICGA showed 1.51±0.86. In the interclass correlation coefficient analysis, the correlation between Set A and ICGA was ICC=0.76(95% CI:0.60-0.86, p<0.001), and the correlation between Set B and ICGA was ICC=0.84(95% CI:0.72-0.91, p<0.001). This study highlights that, without using ICGA, both CFP combined with OCT and CFP combined with SS-OCTA demonstrate high sensitivity, specificity, and AUC in diagnosing PCV. It is evident that SS-OCTA contributes to enhancing sensitivity, specificity, and AUC for PCV diagnosis.