Search

PurposeTo develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.MethodsImage quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC).ResultsThe DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88).ConclusionsThe DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model.Translational relevanceIncorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

Retinal blood vessel segmentation is important for detection of several highly prevalent, vision-threatening diseases such as diabetic retinopathy. Automatic retinal blood vessel segmentation is crucial to overcome the limitations posed by diagnoses by doctors. In recent times, deep learning-based methods have achieved great success in automatically segmenting retinal blood vessels from images. In this paper, a U-Net-based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Three pre-processing algorithms are proposed to enhance the performance of the proposed method further. Based on experimental evaluation of the publicly available DRIVE dataset, the proposed method achieves 0.9577 average accuracies (Acc), 0.7436 sensitivity (Se), 0.9838 specificities (Sp) and 0.7931 F1-score. The proposed method outperforms the recent state-of-art approaches in the literature. [ABSTRACT FROM AUTHOR]

Background: Eales' disease is an idiopathic, inflammatory condition characterized by peripheral retinal phlebitis, distal non-perfusion, and neovascularization. Coats' disease, on the other hand, is an idiopathic, retinal vasculopathy characterized by telangiectasia and yellowish exudation. Both diseases commonly affect males. Here, we report an atypical case of unilateral Eales' disease with co-existing, secondary Coats'-like reaction in a 25-year-old male exhibiting unilateral retinal telangiectasia accompanied by subtle macular edema, extensive peripheral vascular sheathing, and distal non-perfusion in the absence of clinically visible exudation. Methods: Case report. Results: A 25-year-old male presented with painless blurring of vision in the right eye for two months. Best-corrected Snellen visual acuity was 3/60 in the affected eye, with relative afferent pupillary defect observed, and 6/9 contralaterally. Anterior segment examination was unremarkable. Right eye fundoscopy showed aneurysmal telangiectasia at the macula, 360 degrees perivascular sheathing and telangiectasia of peripheral retinal vessels. There was no exudation, neovascularization, tractional membranes, vitritis, retinitis, and choroiditis. Left eye fundus was unremarkable. Optical coherence tomography of the macula revealed disorganization and thinning of the inner retinal layers with minimal intraretinal fluid. Fundus fluorescein angiography showed leakage from the aneurysms but not from the sheathed vessels, and an enlarged foveolar avascular zone. A well-defined zone of non-perfusion and retinal vascular telangiectasia with shunts was observed. Blood investigations ruled out metabolic, infectious, haematological, and autoimmune causes. Carotid Doppler showed no stenosis, and CT angiography of the brain did not show any vascular abnormalities. In view that no cause was identified, a diagnosis of Eales' disease with secondary Coats'-like reaction was made. Treatment consisted of FFA-guided laser photocoagulation to the areas of non-perfusion. Intavitreal anti-vascular endothelial growth factor injection was not done in view of poor visual prognosis. The condition and visual acuity remained stable during 18 months of follow-up. Conclusion: This report highlights an atypical case of Eales' disease with co-existing, secondary Coats'-like reaction. [ABSTRACT FROM AUTHOR]

Aim Method Results Interpretation To longitudinally evaluate the natural history of cerebral visual impairment (CVI) in children with cerebral palsy (CP) and identify which early visual signs or symptoms are associated with cognitive visual disorders (CVDs) at school age.Fifty‐one individuals with CP and CVI underwent an ophthalmological, oculomotor, and basic visual function evaluation at three time points: T0 (6–35 months old); T1 (3–5 years old); and T2 (≥6 years old). We also performed a cognitive visual evaluation at T2. Logistic regression fitted using a generalized estimation equation (binary) and cumulative link models (ordinal) were used to model the outcomes of interest.Ophthalmological deficits were stable over time, except for ocular fundus abnormalities (T1–T0, p = 0.01; T2–T1, p = 0.02; T2–T0, p < 0.01) and strabismus, whose frequency increased with age (T2–T0, p= 0.02 with T2‐T0, p = 0.05). Conversely, fixation (T1–T0, T2–T0, p < 0.01), smooth pursuit (T2–T1, T2–T0, p < 0.01), saccades (T1–T0, T2–T1, T2–T0, p < 0.01), as well as visual acuity, contrast sensitivity, and visual field (T1–T0, T2–T0, p < 0.01) all improved over time. Early oculomotor dysfunction was associated with CVD at T2.Although a diagnosis of CVI was confirmed in all children at each time point, several visual signs and symptoms improved over time; in some cases, they reached complete recovery at T1 and T2. These results emphasize the ‘permanent’ but ‘not unchanging’ nature of the CVI associated with CP during development. [ABSTRACT FROM AUTHOR]

An adult pet tawny owl (Strix aluco) presented to a veterinary hospital at Parma University with a history of head trauma. After a critical care protocol including thermal, oxygen and fluid support aimed at stabilizing the patient, a neurological examination was performed. During neurological evaluation, marked lethargy and an inability to rise from a recumbent position was noted. Anisocoria was also present, with a mydriatic left pupil exhibiting no pupillary light response (PLR) even on direct illumination of both eyes. On ocular fundus examination, retinal hemorrhage and retinal detachment were observed. Based on these clinical findings, a complete work-up was performed, including hematological exams and total body X-ray studies followed by a computed tomography (CT) scan. Additional examinations, such as brainstem auditory evoked response (BAER) measurement and flash visual evoked potential (FVEP) recording, were performed. FVEP measurements performed on the left eye exhibited no peaks in either series of stimulations, indicating an altered functional integration within the visual pathway. A CT scan revealed a large hypoattenuating lesion within the right cerebral hemisphere, suspected to be intraparenchymal edema. The BAER test demonstrated an altered trace consistent with brainstem involvement and left hypoacusis due to cranial nerve VIII deficiency. Head trauma can result in significant neurological impairments in birds, impacting their behavior, mobility, and cognitive abilities. FVEP recordings, BAER tests and CT scans may be useful diagnostic tools in clinical practice. Understanding the causes and neurologic presentation of avian traumas is essential for effective prevention, diagnosis and treatment of affected birds. [ABSTRACT FROM AUTHOR]

One of the most prevalent influenceable risk factors for poor cardiovascular outcome is arterial hypertension. This is a prospective analysis of liver transplant recipients in which 24-hour blood pressure (BP) measurement was performed. The primary aim was to identify post-LT (liver transplantation) patients without a history of arterial hypertension who meet the criteria for arterial hypertension using 24-hour BP monitoring. Secondary objectives were to determine how many patients with known treated arterial hypertension had suboptimal BP control. The group included 88 patients (men, 52.3%, history of arterial hypertension group: n=56, no history of arterial hypertension group: n=32) with an average age at the time of measurement of 62.4 years±11. The average time since LT at the time of measurement was 45.2 months. De novo arterial hypertension using 24-hour BP monitoring was diagnosed in 28%. Hypertonic changes in the fundus oculi were confirmed as predictor for suboptimally controlled hypertension [OR 5,1265, p=0.0279]. On the other hand, male sex [OR 3.1840, p=0.0311], together with age [OR 1.3347, p=0.0153] and waist circumference [OR 4.3490, p=0.0418] predicted the need of intensification of antihypertensive treatment. Male sex, age and waist circumference should increase the index of suspicion and lead to zoom-in on a possibility of poorly controlled blood pressure. Where automated blood pressure monitoring is unavailable, regular examination of the fundus could serve as an available surrogate marker of suboptimally controlled arterial hypertension [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationship between the fundus sex index obtained from fundus photographs and body height or axial length in the Kumejima population. Study Design: Prospective cross-sectional observational population study. Methods: Using color fundus photographs obtained from the Kumejima population, 1,653 healthy right eyes with reliable fundus parameter measurements were included in this study. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in the eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. The masculine or feminine fundus was quantified using machine learning (L2 regularized binominal logistic regression and leave one out cross validation), with the range of 0–1 as the predictive value, and defined as the fundus sex index. The relationship between the fundus sex index and body height or axial length was investigated using Spearman's correlation. Results: The mean age of the 838 men and 815 women included in this study was 52.8 and 54.0 years, respectively. The correlation coefficient between fundus sex index and body height was − 0.40 (p < 0.001) in all, 0.01 (p = 0.89) in men, and − 0.04 (p = 0.30) in women, and that between fundus sex index and axial length was − 0.23 (p < 0.001) in all, − 0.12 (p < 0.001) in men, and − 0.13 (p < 0.001) in women. Conclusion: This study shows that a larger number of masculine fundi tend to have longer axial lengths in each sex group. However, sex index was not significantly related with body height either in men or in women. [ABSTRACT FROM AUTHOR]

Recent advancements in clinical research have identified the need to combine pupillometry with a selective stimulation of the eye's photoreceptor cell types to broaden retinal and neuroretinal health assessment opportunities. Our thorough analysis of the literature revealed the technological gaps that currently restrict and hinder the effective utilization of a method acknowledged to hold great potential. The available devices do not adequately stimulate the photoreceptor types with enough contrast and do not guarantee seamless device function integration, which would enable advanced data analysis. RetinaWISE is an advanced silencing pupillometry device that addresses these deficiencies. It combines a Maxwellian optical arrangement with advanced retinal stimulation, allowing for calibrated standard measurements to generate advanced and consistent results across multiple sites. The device holds a Class 1 CE marking under EU regulation 2017/745, thus facilitating clinical research progress. [ABSTRACT FROM AUTHOR]

Copyright of Arquivos Brasileiros de Oftalmologia is the property of Arquivos Brasileiros de Oftalmologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blood vessels in the retina become damaged as a result of diabetes. If it is not treated early, then it leads to Diabetic retinopathy (DR), which is a significant reason for blindness. Clinical tests are needed to detect and classify the stages, but these are very expensive and time-consuming. This paper proposes an enhanced Gaussian distribution-based image enhancement and segmentation to detect diabetic retinopathy. Medical images have high noise and artefacts, making it difficult to recognize the details with the normal eye and very tough to process the image further. The existing approaches to enhance medical images are very complex and time-consuming. We proposed a more effectiveenhanced Gaussian distribution approach to overcome this limitation. The lesions' inconsistent size and noise in the fundus image make it challenging to segment the lesion. In this study, an improved regularization function in the convolution neural network(CNN) was applied to the retina images to detect the lesions automatically and reduce the overfitting, reducing loss. An improved Support Vector Machine(ISVM) classifies the segmented fundus images. The proposed improved CNN model is compared with the previously carried out studies, and the results prove that the improved CNN model is efficient in diagnosing diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a common eye disease that results in vision loss by damaging the blood vessels. Diabetic patients are at high risk of developing DR owing to the damage of retinal lesions, thereby causing clots, injuries and bleeding. The disease leads to abnormal changes in the structure of the retina. Therefore, the timely detection and early treatment of eye diseases are essential for preventing people from vision loss. Ophthalmologists distinguish DR based on features such as exudes, microaneurysms, blood vessel area, hemorrhages, etc. An artificial intelligence (AI) method is proposed by ensembling a deep learning (DL) model with the explainable AI based Shapley additive (SHAP) method for DR image segmentation and classification. The proposed model uses fundus images to detect abnormalities in the eye. First, the DR images are collected from the Asia Pacific Tele-Ophthalmology Society 2019 blindness detection (APTOS 2019) dataset. Data augmentation is performed to artificially increase the size of a training dataset by generating new data samples from existing ones by rescaling, flipping, rotating, zooming, etc. In order to improve the quality and enhance specific features, pre-processing is performed. The pre-processed images are segmented using the improved U-Net model, where the severity of the disease gets predicted, and the fundus images are segmented using the trained model to attain precise abstraction of retinal blood vessels. Finally, the proposed study used the Shapley Additive Ensembled DenseNet-121 ResNet-50 (SAE-DR) model to detect DR disease based on the features. To improve the readability of the deep learning model, an explainable AI based Shapley additive method is proposed. Then, compare the results of the proposed model with existing state-of-the-art methods. The simulation results prove that the proposed model achieves superior detection performance with an accuracy of 98.69%, sensistivity of 86.23%, specificity of 97.54%, F-score of 90.26%, precision of 94.26% and processing time of 0.153 s. [ABSTRACT FROM AUTHOR]

Globally, eye disorders have been a major issue, especially in developing nations where resources for technology and financing are limited. Due to its tremendous feature learning ability, CNN has obtained remarkable progress in the area of fundus images. Through appropriate analysis and examination of fundus images, computer-aided diagnosis may yield information with a standard value for experts in clinical diagnosis or screening. However, the majority of earlier investigations have focused on the identification of a particular fundus disease, and the accurate fast classification of multiple fundus diseases remains a major challenge. Extremely numerous retinal fundus images must be analyzed to achieve a categorization that is reliable, fast, and precise. As a result, this research aims to propose a novel classification model for eye illnesses based on four major steps: (a) Pre-processing, (b) segmentation of blood vessels, (c) Extracting features and (d) Multi-classification of eye disease. In order to accurately classify the images, it is necessary to extract the essential informational characteristics from the segmented blood vessels. The categorization of eye diseases is carried out using a hybrid classifier that integrates the SqueezeNet and Long-Term Recurrent Convolutional Network (LRCN). We trained and validated our models using tenfold cross-validation tests on a database of fundus images including five categories: Normal, Retinitis Pigmentosa, Pathological Myopia, Maculopathy and Glaucoma. The efficacy of the presented approach is evaluated based on specificity, F1-score, recall, precision and accuracy of 98%, 97.8%, 97.6%, 98.4% and 98% respectively. [ABSTRACT FROM AUTHOR]

Retinal diseases that are not treated in time can cause irreversible, permanent damage, including blindness. Although a patient may suffer from more than one retinal disease at the same time, most of the studies focus on the diagnosis of a single disease only. Therefore, to detect multi-label retinal diseases from color fundus images, we developed an end-to-end deep learning architecture that combines the EfficientNet backbone with the ML-Decoder classification head in this study. While EfficientNet provides powerful feature extraction with fewer parameters via compound scaling, ML-Decoder further improves efficiency and flexibility by reducing quadratic dependency to a linear one and using a group decoding scheme. Also, with the use of sharpness-aware minimization (SAM) optimizer, which minimizes loss value and loss sharpness simultaneously, higher accuracy rates have been reached. In addition, a significant increase in EfficientNet performance is achieved by using image transformations and concatenation together. During the training phase, the random application of the image transformations allows for increasing the image diversity and makes the model more robust. Besides, fusing fundus images of left and right eyes at the pixel level extracts useful information about their relationship. The performance of the final model was evaluated on the publicly available Ocular Disease Intelligent Recognition (ODIR) dataset consisting of 10,000 fundus images, and superior results were obtained in all test set scenarios and performance metrics than state-of-the-art methods. The best results we obtained in the threefold cross-validation scenario for the kappa, F1, and AUC scores are 68.96%, 92.48%, and 94.80%, respectively. Moreover, it can be considered attractive in terms of floating point operations per second (FLOP) and a number of parameters. [ABSTRACT FROM AUTHOR]

Background: Toxic anterior segment syndrome (TASS) is a rare, noninfectious inflammation that occurs after anterior segment surgery. We report a case herein that developed presumed atypical late-onset TASS after V4c implantable collamer lens (ICL) implantation surgery. Case presentation: A 26-year-old man underwent ICL implantation surgeries of both eyes on two separate days. The 1-day and 7-day postoperative routine follow-up visits revealed no abnormalities. However, one month after surgery, dense white spots attached to the posterior surface and scattered ones to the anterior surface of ICL in the left eye were noted on anterior segment examination. His uncorrected distance visual acuity (UDVA) was 20/16 in both eyes and the fundus examination was normal. Despite the absence of typical clinical manifestations, late-onset TASS was suspect and intense topical steroid was administered. After 6 weeks of tapering topical steroid therapy, the white spots disappeared and the patient had no subjective complains throughout the treatment period. Conclusions: This case suggested that the traditionally considered acute and serious TASS could also present as delayed and insidious onset after ICL implantation surgery. Due to its variabilities, the awareness of TASS should be raised to ophthalmologists and regular follow-up visits should be emphasized to patients. Once TASS was suspected, intensive steroid therapy should be implemented in time. [ABSTRACT FROM AUTHOR]

Cataracts are common eye disorders characterized by the clouding of the lens, preventing light from passing through and impairing vision. Various factors, including changes in the lens's hydration or alterations in its proteins, may contribute to their development. Regular eye examinations conducted by an ophthalmologist or optometrist are imperative for detecting cataracts and other ocular conditions early on. Manual checks by caregivers pose several problems, including subjectivity, human error, and a lack of expertise. Biomedical fusion involves combining or linking various characteristics specific to certain diseases from different medical imaging resources. The primary objectives of this approach in disease classification are to reduce the error rate and increase the number of retrieved features. The aim of this study is to evaluate the outcomes associated with fusing visual features related to left and right eye cataract characteristics. Additionally, we investigate the impact of limited variability in deep learning models, specifically in the classification of cataract fundus versus normal fundus images. To address this issue, this study introduces CataractNetDetect, an innovative multi-label deep learning classification system that fuses feature representations from pairs of fundus images (e.g., left and right eyes) for the automatic diagnosis of various ocular disorders. Our focus is on achieving improved performance by stacking discriminative deep feature representations to combine two fundus images into a unified feature representation. Several deep learning architectures are utilized as feature descriptors, including ResNet-50, DenseNet-121, and Inception-V3, enhancing the resilience and quality of representations. Fine-tuning of these DL architectures is conducted using the ImageNet dataset, followed by an integrated stacking approach combining ResNet-50, DenseNet-121, and Inception-V3 models. The model is trained on the publicly available ODIR-5k dataset, which includes 5000 left/right eye images depicting eight different ocular conditions, ranging from healthy states to uncommon ailments such as cataracts, glaucoma, age-related macular degeneration (AMD), diabetes, hypertension, and myopia abnormalities. Moreover, extensive preprocessing of the images is performed, including data augmentation, noise reduction, contrast enhancement, scaling, and circular border cropping. The CataractNetDetect system demonstrates F1-scores, AUC, and maximum validation scores of 98.0%, 97.9%, and 100%, respectively. This ensemble-based model distinguishes itself by surpassing the performance of conventional established methodologies, including ResNet-50, DenseNet-121, and Inception-V3, thereby underscoring its efficacy in diagnostic applications. [ABSTRACT FROM AUTHOR]

Sensing gravity through the otolith receptors is crucial for bipedal stability and gait. The overall contribution of the otolith organs to eye movements, postural control, and perceptual functions is the basis for clinical testing of otolith function. With such a wide range of contributions, it is important to recognize that the functional outcomes of these tests may vary depending on the specific method employed to stimulate the hair cells. In this article, we review common methods used for clinical evaluation of otolith function and discuss how different aspects of physiology may affect the functional measurements in these tests. We compare the properties and performance of various clinical tests with an emphasis on the newly developed video ocular counter roll (vOCR), measurement of ocular torsion on fundus photography, and subjective visual vertical or horizontal (SVV/SVH) testing. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore retinal vasculature asymmetry (ReVA) patterns in subjects from the islands of Vis and Korcula and the city of Split, Croatia. Asymmetry estimates were based on topographic image analysis of non-mydriatic retinal fundus photographs and compared with nine ophthalmic measurements, three Doppler-based pressure indices and eight frequencies of audiometry. ReVA was also correlated to the genomic runs of homozygosity (ROHs) and used in a Cox regression survival model, where we adjusted for the effects of sex, age and comorbidity. In 1873 subjects, ReVA estimates were significantly correlated with most ophthalmic asymmetry measures, less strongly with the ankle–brachial pressure index and only modestly with higher-amplitude audiometry asymmetries (lowest p = 0.020). ReVA was significantly correlated with the number of ROHs (r = 0.229, p < 0.001) but less strongly with the ROH length (r = 0.101, p < 0.001). The overlap of asymmetries was low, with only 107 subjects (5.7% of the total sample) who had two or more instances in which they were among the top 10%. Multiple asymmetries did not affect survival (HR = 0.74, 95% confidence intervals 0.45–1.22). Retinal vasculature asymmetry is a poor predictor of asymmetry elsewhere in the body. Despite its existence and apparent association with comorbidities, the observed extent of retinal vasculature asymmetry did not affect the lifespan in this population. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to compare geographic atrophy (GA) area semi-automatic measurement using fundus autofluorescence (FAF) versus optical coherence tomography (OCT) annotation with the cRORA (complete retinal pigment epithelium and outer retinal atrophy) criteria. Methods: GA findings on FAF and OCT were semi-automatically annotated at a single time point in 36 pairs of FAF and OCT scans obtained from 36 eyes in 24 patients with dry age-related macular degeneration (AMD). The GA area, focality, perimeter, circularity, minimum and maximum Feret diameter, and minimum distance from the center were compared between FAF and OCT annotations. Results: The total GA area measured on OCT was 4.74 ± 3.80 mm2. In contrast, the total GA measured on FAF was 13.47 ± 8.64 mm2 (p < 0.0001), with a mean difference of 8.72 ± 6.35 mm2. Multivariate regression analysis revealed a significant correlation between the difference in area between OCT and FAF and the total baseline lesion perimeter and maximal lesion diameter measured on OCT (adjusted r2: 0.52; p < 0.0001) and the total baseline lesion area measured on FAF (adjusted r2: 0.83; p < 0.0001). Conclusions: We report that the GA area measured on FAF differs significantly from the GA area measured on OCT. Further research is warranted in order to determine the clinical relevance of these findings. [ABSTRACT FROM AUTHOR]

Context The paucity of data on microvascular complications in patients with posttransplant diabetes (PTDM) is an obstacle to developing follow-up algorithms. Objective To evaluate diabetic microvascular complications in patients with long-standing PTDM. Methods In patients with ≥5-year history of PTDM and age-matched renal transplant recipients without PTDM (NDM), diabetic peripheral neuropathy was evaluated using the Michigan Neuropathy Screening Instrument, the CASE IV device, and in vivo corneal confocal microscopy (CCM). Cardiac autonomic neuropathy tests were performed using heart rate variability. Nephropathy screening was assessed using spot urine albumin/creatinine ratio and eGFR calculation. Diabetic retinopathy was evaluated by fundus examination and photography, and optical coherence tomography. Results This study included 41 patients with PTDM and 45 NDM patients. The median follow-up was 107.5 months in the PTDM group. Peripheral neuropathy was significantly higher in the PTDM group than in the NDM group (P =.02). In the PTDM patients with peripheral neuropathy, corneal nerve fiber density examined by CCM was significantly lower than in PTDM patients without neuropathy (P =.001). Parasympathetic involvement was observed in 58.5% of the PTDM group and 22% of the NDM group (P =.001). Sympathetic involvement was present in 65.9% of the PTDM group and 29.3% of the NDM group (P =.001). Retinopathy was observed in 19.5% of patients in the PTDM group and in none of the NDM patients (P <.001). Renal functions were similar between the study groups. Conclusion Cardiac autonomic neuropathy and diabetic retinopathy can affect patients with PTDM at a high rate. Diabetic retinopathy is a threat to the vision of PTDM patients. Diabetic peripheral neuropathy can be detected early in PTDM patients by CCM. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems. Ocular involvement is estimated to manifest in one-third of individuals with SLE, of which lupus retinopathy and choroidopathy represent the severe subtype accompanied by vision impairment. Advancements in multimodal ophthalmic imaging have allowed ophthalmologists to reveal subclinical microvascular and structural changes in fundus of patients with SLE without ocular manifestations. Both ocular manifestations and subclinical fundus damage have been shown to correlate with SLE disease activity and, in some patients, even precede other systemic injuries as the first presentation of SLE. Moreover, ocular fundus might serve as a window into the state of systemic vasculitis in patients with SLE. Given the similarities of the anatomy, physiological and pathological processes shared among ocular fundus, and other vital organ damage in SLE, such as kidney and brain, it is assumed that ocular fundus involvement has implications in the diagnosis and evaluation of other systemic impairments. Therefore, evaluating the fundus characteristics of patients with SLE not only contributes to the early diagnosis and intervention of potential vision damage, but also holds considerate significance for the evaluation of SLE vasculitis state and prediction of other systemic injuries. [ABSTRACT FROM AUTHOR]

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AIs potential in diagnosing etiologies of pediatric retinal hemorrhages.

Copyright of Arquivos Brasileiros de Oftalmologia is the property of Arquivos Brasileiros de Oftalmologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Macular Telangiectasia Type 2 (MacTel) is a chronic, progressive disease of the central retina characterized by vascular and neurodegenerative changes. As there is currently no treatment for non-neovascular MacTel, there is a dearth for biomarkers identifying eyes with an increased risk for disease progression for patient counseling and clinical trial recruitment. Eyes were classified to be stable or progressive, defined by the fundus photography-based grading system by Gass and Blodi. First, structural differences between these two groups were assessed, employing optical coherence tomography (OCT) and OCT-angiography. Univariate regression analyses revealed evidence towards a lower superficial retinal layer (SRL) vessel density (VD), skeleton density (SD) and deep retinal layer (DRL) SD in progressing compared to stable eyes (p = 0.05, p = 0.05, p = 0.07). Second, a multivariable predictive model was employed to examine the predictive value of structural and functional parameters for disease progression. Baseline best corrected visual acuity (BCVA) and SRL SD are prognostic for disease progression (p < 0.001, p = 0.05). The presence of ellipsoid zone (EZ) loss is prognostic for future central retinal thickness (p < 0.01). We propose SRL SD, BCVA, and EZ loss as prognostic biomarkers and as possible outcome measures in future interventional studies in MacTel. [ABSTRACT FROM AUTHOR]

This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were of satisfactory diagnostic quality. Risk factors for poor image quality included older age, the presence of cataracts, pseudophakia, and diabetes mellitus. This study demonstrates the feasibility of using non-mydriatic fundus cameras for teleophthalmology, highlighting the importance of identifying and addressing factors that affect image quality to enhance diagnostic accuracy in remote settings. [ABSTRACT FROM AUTHOR]

Purtscher retinopathy is a rare but severe sight-threatening eye condition that mostly occurs in middle-aged men after chest compression or head injury. In cases such as acute pancreatitis, connective tissue disorders, kidney failure or COVID-19 infection with similar ocular findings but no history of trauma, a diagnosis of Purtscher-like retinopathy is made. We present a case of a 72-year-old female with typical symptoms of Purtscher retinopathy in both eyes after a car crash accident. Although the pathophysiology of the disease is not fully understood, the main cause of Purtscher retinopathy seems to be an embolic occlusion of the precapillary arterioles which supply the superficial peripapillary capillaries. Activation of the C5a component of the complement predisposes the leukocytes to aggregation, which obstructs blood flow. The main symptom of Purtscher retinopathy is sudden, painless deterioration of vision which occurs up to 48 h after the injury. In most patients, the changes observed in the fundus of the eye resolve within several months, and visual acuity slowly improves, sometimes even returning to the state from before the injury. However, risk factors such as older age, high hyperopia, and late treatment implementation can make the prognosis less favorable. [ABSTRACT FROM AUTHOR]

Ocular diseases can range in severity, with some being more serious than others. As a matter of fact, there are several common and severe eye diseases, for instance, glaucoma, i.e., a group of eye conditions that damage the optic nerve, often associated with elevated intraocular pressure. Effective management and prevention strategies require a multifaceted approach, involving healthcare providers, public health officials and community education. Regular screenings and early interventions are crucial in reducing the impact of eye diseases on individuals and populations. In this paper, we propose a method aimed to detect the presence of ocular disease from the automatic analysis of eye fundus photographs. We consider deep learning; in detail, we adopt several convolutional neural networks aimed to train several models to be able to discriminate between different eye diseases. Furthermore, to boost the application of deep learning in real-world everyday medical practice, we adopt a method to understand which areas of the images are of interest from the model's point of view; this allows us to detect disease by providing in this way disease localization by explainability. In the experimental analysis, we provide a set of four different experiments: in the first one, we propose a model to discern between age-related macular degenerations and normal fundus (obtaining an accuracy of 0.91); in the second one, the model is able to discriminate between cataract and normal fundus (obtaining an accuracy of 0.92); the third experiment is related to a model aimed to discriminate between glaucoma and normal ocular fundus (obtaining an accuracy of 0.88); and the last experiment is related to a model aimed to discern between pathological myopia and normal ocular fundus (obtaining an accuracy of 0.95). Thus, the experimental analysis confirms the effectiveness of the proposed method from a quantitative point of view (analysis aimed at understanding whether the model is able to correctly identify the disease) but also from a qualitative one, with a detailed and reasoned analysis aimed at understanding whether the model is able to correctly localize the disease. [ABSTRACT FROM AUTHOR]

Background: As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy. Methods: In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. Results: The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. Conclusion: The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article presents a study on a new genetic rearrangement that is linked to a condition called PRPF31-associated retinitis pigmentosa (RP). RP is a genetic eye disorder that causes the degeneration of certain cells in the retina, leading to vision problems. The researchers discovered a specific deletion on chromosome 19 that includes the PRPF31 gene, which is associated with RP. They also observed reduced levels of PRPF31 in RP patients. This research offers valuable insights into the genetic causes of RP and could potentially contribute to the development of targeted treatments. Additionally, the study explores a specific mechanism called Alu/Alu-mediated rearrangement (AAMR) that is responsible for this genetic rearrangement. [Extracted from the article]

The pecten is a fold‐structured projection at the ocular fundus in bird eyes, showing morphological diversity between the diurnal and nocturnal species. However, its biological functions remain unclear. This study investigated the morphological and histological characteristics of pectens in wild birds. Additionally, the expression of non‐visual opsin genes was studied in chicken pectens. These genes, identified in the chicken retina and brain, perceive light periodicity regardless of visual communication. Similar pleat numbers have been detected among bird taxa; however, pecten size ratios in the ocular fundus showed noticeable differences between diurnal and nocturnal birds. The pectens in nocturnal brown hawk owl show extremely poor vessel distribution and diameters compared with that of diurnal species. RT‐PCR analysis confirmed the expression of Opn5L3, Opn4x, Rrh and Rgr genes. In situ hybridization analysis revealed the distribution of Rgr‐positive reactions in non‐melanotic cells around the pecten vessels. This study suggests a novel hypothesis that pectens develop dominantly in diurnal birds as light acceptors and contribute to continuous visual function or the onset of periodic behaviour. [ABSTRACT FROM AUTHOR]

Curcuma aeruginosa (CA) rhizomes are used in traditional medicine as gastrointestinal remedies, postpartum care and uterine issues. However, its effects on gastrointestinal motility have not yet been explored. Extracts of CA rhizomes prepared using chloroform, methanol and water were evaluated for their effects on gastrointestinal contractions using isolated rat gastric fundus and ileum. Varied solvents aim for extraction of different compounds of CA rhizome. In the ileum, the 3 extracts did not significantly affect spontaneous contractions, but significantly reduced contractions induced by acetylcholine (ACh, 0.3 μM) and KCl (40 mM) in a concentration-dependent manner. The order of potency, based on ACh-induced contraction, was chloroform > methanol > water extracts, with respective IC50 values of 0.025 ± 0.001, 0.46 ± 0.02 and 2.57 ± 0.47 mg/mL. Similar potency order against KCl-induced contraction was observed, with IC50 values of 0.011 ± 0.002, 0.47 ± 0.06 and 1.18 ± 0.18 mg/mL, respectively. In the isolated gastric fundus model, both methanol and water extracts resulted in an increase in the force of contraction in the absence of stimulation. However, when ACh (0.3 μM) was introduced, the methanol and water extracts exhibited a concentration-dependent reduction in contractions, with IC50 values of 0.62 ± 0.06 and 2.30 ± 0.32 mg/mL, respectively. As for KCl (40 mM)-induced contraction, only the highest concentration of the water extract (3.75 mg/mL) significantly decreased the contraction by 41.29 ± 2.94 %. CA extract demonstrated relaxant effects on both isolated rat gastric fundus and ileal contractions induced by ACh and KCl. These findings support the use of CA in traditional medicine as a gastrointestinal remedy. These extracts have the potential to be used as antispasmodics and to treat infectious and non-infectious diarrhea, the latter of which occurs in functional gastrointestinal diseases. It might also be useful as a gastroprokinetic. [ABSTRACT FROM AUTHOR]

Purpose: Extraocular electrical stimulation is known to provide neuroprotection for retinal cells in retinal and optic nerve diseases. Currently, the treatment approach requires patients to set up extraocular electrodes and stimulate potentially weekly due to the lack of an implantable stimulation device. Hence, a minimally-invasive implant was developed to provide chronic electrical stimulation to the retina, potentially improving patient compliance for long-term use. The aim of the present study was to determine the surgical and stimulation safety of this novel device designed for neuroprotective stimulation. Methods: Eight normally sighted adult feline subjects were monocularly implanted in the suprachoroidal space in the peripheral retina for 9--39 weeks. Charge balanced, biphasic, current pulses (100 μA, 500 µs pulse width and 50 pulses/s) were delivered continuously to platinum electrodes for 3--34 weeks. Electrode impedances were measured hourly. Retinal structure and function were assessed at 1-, 2-, 4-, 6- and 8-month using electroretinography, optical coherence tomography and fundus photography. Retina and fibrotic thickness were measured from histological sections. Randomized, blinded histopathological assessments of stimulated and non-stimulated retina were performed. Results: All subjects tolerated the surgical and stimulation procedure with no evidence of discomfort or unexpected adverse outcomes. The device position was stable after a post-surgery settling period. Median electrode impedance remained within a consistent range (5-10 kΩ) over time. There was no change in retinal thickness or function relative to baseline and fellow eyes. Fibrotic capsule thickness was equivalent between stimulated and non-stimulated tissue and helps to hold the device in place. Therewas no scarring, insertion trauma, necrosis, retinal damage or fibroblastic response in any retinal samples from implanted eyes, whilst 19% had a minimal histiocytic response, 19% had minimal to mild acute inflammation and 28% had minimal to mild chronic inflammation. Conclusion: Chronic suprathreshold electrical stimulation of the retina using a minimally invasive device evoked a mild tissue response and no adverse clinical findings. Peripheral suprachoroidal electrical stimulation with an implanted device could potentially be an alternative approach to transcorneal electrical stimulation for delivering neuroprotective stimulation. [ABSTRACT FROM AUTHOR]

Avoidance of PCR with prior AI detection. PPC projection on UWF fundus image. AI detection of PPC projection to avoid cataract surgical catastrophe. Purpose: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images. Setting: Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan. Design: Retrospective chart review. Methods: A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization. Results: The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103). Conclusions: This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery. [ABSTRACT FROM AUTHOR]

The prevalence of choroidal naevi is common and has been found to be up to 10%. Little is known regarding the optical properties of choroidal naevi. A novel hyperspectral eye fundus camera was used to investigate choroidal naevi's optical density spectra in the retina. In an ophthalmology clinic setting, patients with choroidal naevi were included in the study. Visual acuity and pressure were tested. Following mydriatics, optical coherence tomography and fundus photography were taken as a reference, after which a hyperspectral image with 12 nm spectral resolution at 450–700 nm was taken. The optical density spectra was measured across the area of the naevus. Nine patients with 11 naevi were examined. The visual acuity was not affected by any of the naevi. All the naevi were flat as measured either with the optical coherence tomography and/or on inspection, and only one naevi had a risk factor (orange pigmentation). The Wasserstein distance between the background and the naevi was higher at 695 nm compared to 555 nm (p =.002). The naevi could be grouped into three clusters based on the extracted optical density spectra. Choroidal naevi are better visible in longer wavelengths compared to shorter wavelengths. This finding can be used to contour and follow choroidal naevi. Choroidal naevi expose different optical density spectra that can be grouped into three different clusters. One of these clusters has an optical density spectra resembling the absorption spectra of lipofuscin, which may indicate the content of this pigment. [ABSTRACT FROM AUTHOR]

The extensor digitorum profundus complex underwent degeneration of the ulnar segments during primate adaptation and evolution. This process resulted in the preservation of only the extensor pollicis longus and extensor indicis in some apes, including humans. Consequently, anatomical variations within the digitorum profundus complex in modern humans have been well-documented, with detailed reports on their frequency and patterns in previous studies. Here, we report an unusual arrangement involving two anomalies in the extensor digitorum profundus complex, identified in a 66-year-old Japanese male cadaver. In this cadaver, two accessory muscles differentiated from both the extensor pollicis longus and extensor indicis. Notably, the latter muscle featured a tendon bifurcating towards both the thumb and index fingers, referred to as the extensor pollicis et indicis communis. Under the extensor retinaculum, the tendon of the accessory extensor pollicis longus passed through an independent compartment, whereas that of the extensor pollicis et indicis communis traversed a compartment shared by the extensor indicis and the extensor digitorum communis. Both muscles were innervated by the posterior interosseous nerve. Previous studies have reported that the accessory slip of the extensor pollicis longus and extensor pollicis et indicis communis appear at frequencies of 0.6% and 0.4–1.4%, respectively. However, to the best of our knowledge, a configuration in which both appear simultaneously has not been reported. The data from this case could provide essential insights into the variations in the extensor digitorum profundus complex in humans and non-human primates. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age. Biased medical artificial intelligence systems pose significant potential risks to achieving fair and equitable healthcare. Here, we demonstrate a fairer ophthalmology AI that mitigates sexism and ageism through implicit fairness learning. [ABSTRACT FROM AUTHOR]

In virtual reality (VR), a factor that can maximize user immersion is the development of an intuitive and sensory interaction method. Physical devices such as controllers or data gloves of existing VR devices are used to control the movement intentions of the user, but their shortfall is that grip strength and detailed muscle strength cannot be reflected. Therefore, this study intended to establish a more sensory VR environment compared to existing methods by reflecting the grip strength of the flexor digitorum profundus of the user of the VR content. In this experiment, the muscle activity of the flexor digitorum profundus was obtained from six subjects based on surface electromyography, and four objects with differing intensity were created within a VR program in which the objects were made to be destroyed depending on muscle activity. As a result, satisfaction was improved because the users could sensitively interact with the objects inside the VR environment, and the intended motion control of the user was reflected in the VR content. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the foremost reasons for poor eyesight in the modern globe. An earlier detection of DR is vital in offering effectual treatment. Moreover, detecting the severity levels of DR, such as Microaneurysms (MA), Hemorrhages (HMs), Exudates (EXs) and extra development of blood vessel recognition using fundus images are challenging chores owing to the complex structures and shapes of lesions in the fundus image. This paper aims to develop a technique for DR detection at an early stage. MA is the first symptom of DR that leads to blood leakage in the retina. Here, Fractional Hunter Osprey Optimization-Deep Convolutional Neural Network (FHOO-DCNN) is introduced for MA detection. An input image pre-processing is executed by an adaptive wiener filter and then, optic disc (OD) detection is accomplished. The Bayesian U-Net is used for OD detection, which is tuned by Hunter Osprey Optimization (HOO). The HOO is modelled by combining Hunter–Prey Optimizer (HPO) with the Osprey Optimization Algorithm (OOA). Blood vessel segmentation is conducted utilizing morphological Top-Hat transform. Thereafter, features from the input image, blood vessel segmented image, and OD-detected image are extracted. At last, MA detection is performed by DCNN that is tuned using FHOO. Furthermore, FHOO is joining of Fractional Calculus (FC) concept with HOO. In addition, FHOO-DCNN has acquired high accuracy, sensitivity and specificity of 91.1%, 91.2% and 90.4%. The proposed method is applicable in primary screening and regular clinical work for the monitoring of the progression of DR. [ABSTRACT FROM AUTHOR]

Featured Application: The Deep Neural Network model employed in this work will help build a system for the pre-diagnosis of retinopathies that can lead to blindness. The main intention of this type of system is to support the work performed by ophthalmology specialists. Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through fundus images is age-related macular degeneration, which initially manifests as the appearance of lesions called drusen. Several ways of pre-diagnosing macular degeneration have been proposed, methods based entirely on the segmentation of drusen with prior image processing have been designed and applied, and methods based on image pre-processing and subsequent conversion to feature vectors, or patterns, to be classified by a Machine-Learning model have also been developed. Finally, in recent years, the use of Deep-Learning models, particularly Convolutional Networks, has been proposed and used in classification problems where the data are only images. The latter has allowed the so-called transfer learning, which consists of using the learning achieved in the solution of one problem to solve another. In this paper, we propose the use of transfer learning through the Xception Deep Convolutional Neural Network to detect age-related macular degeneration in fundus images. The performance of the Xception model was compared against six other state-of-the-art models with a dataset created from images available in public and private datasets, which were divided into training/validation and test; with the training/validation set, the training was made using 10-fold cross-validation. The results show that the Xception neural network obtained a validation accuracy that surpasses other models, such as the VGG-16 or VGG-19 networks, and had an accuracy higher than 80% in the test set. We consider that the contributions of this work include the use of a Convolutional Neural Network model for the detection of age-related macular degeneration through the classification of fundus images in those affected by AMD (drusen) and the images of healthy patients. The performance of this model is compared against other methods featured in the state-of-the-art approaches, and the best model is tested on a test set outside the training and validation set. [ABSTRACT FROM AUTHOR]

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10−8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images. Author summary: Imaging genetics is a research field focused on understanding how genetic variations influence observable traits or diseases that can be visualized through medical imaging. Previous studies in imaging genetics have mostly relied on traits identified by human experts. In this study, we used a self-supervised contrastive learning algorithm that automatically identifies features in fundus images that are unique to each individual yet consistent between their left and right eyes. These features are represented as a set of 128 numbers that can quantitatively describe the characteristics of each individual's fundus images without human labeling bias. We then conducted genome wide association studies (GWAS) to explore the associations between these features and genetic variations using paired fundus images and genetic data from the UK Biobank. Our findings indicate that these features are linked to genetic signals associated with eye measurements, eye diseases, pigmentation, and vessel development. Additionally, we found that GWAS of this set of features identified new genetic signals not detected in GWAS only using retina color. These results establish the feasibility of this self-supervised phenotyping approach for imaging genetics studies. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) and Glaucoma are two of the most common causes of vision loss world-wide. However, it can be averted if therapy is begun early enough. In biomedical applications, the use of digital image processing has assisted in the automated identification of some ailments at an earlier stage. To make this prediction generally neural network classifier models were previously used, but these models have the drawback of being unable to detect multiple illnesses that occur in the eye at the same time and require a big database for successful classifier training. As a result, a model is needed to reliably distinguish DR and Glaucoma in diabetic individuals more accurately and with minimum dataset images. In this view, this study introduced Mayfly Optimized Deep Convolutional Network (MODCN) model for automated disease detection in the fundus retina images. In the MODCN model, the images are initially preprocessed, segmented at generator in the GAN model then a discriminator readily gives synthesis of real images of the fundus retina, thus a wide database has been created and considered as training images for the MODCN classifier. MODCN classifier has a modified high-density layer as a transition layer to avoid overfitting and the errors are minimized by tuning the hyperparameters using Mayfly Optimization Algorithm. After feature mapping, the classes normal, DR and Glaucoma are labeled and stored. At the testing stage, images are preprocessed, feature mapped and classified in the MODCN model. Thus, the proposed MODCN model detects multiple illness such as Diabetic Retinopathy and Glaucoma at the same time even with a small amount of database that performs a successful classifier training. This model is then evaluated and gives an accuracy of 99% that was higher compared to previous models. [ABSTRACT FROM AUTHOR]

The escalating incidence of diabetes mellitus has amplified the global impact of diabetic retinopathy. There are known structural and functional changes in the diabetic retina that precede the fundus photography abnormalities which currently are used to diagnose clinical diabetic retinopathy. Understanding these subclinical alterations is important for effective disease management. Histology and high‐resolution clinical imaging reveal that the entire neurovascular unit, comprised of retinal vasculature, neurons and glial cells, is affected in subclinical disease. Early functional manifestations are seen in the form of blood flow and electroretinography disturbances. Structurally, there are alterations in the cellular components of vasculature, glia and the neuronal network. On clinical imaging, changes to vessel density and thickness of neuronal layers are observed. How these subclinical disturbances interact and ultimately manifest as clinical disease remains elusive. However, this knowledge reveals potential early therapeutic targets and the need for imaging modalities that can detect subclinical changes in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: Retinal vasculitis (RV) signifies the inflammation of various retinal vessels. Noninfectious RV differs from infectious RV with regard to its pathogenesis and treatment. It can have varied clinical presentations and may be associated with systemic vasculitic diseases. Summary: Noninfectious RV can be caused due to type-III hypersensitivity reactions, increased expression of intracellular adhesion molecules, and genetic susceptibility. Noninfectious RV is primarily classified on the basis of the type of retinal vessels involved. It can be further classified as an occlusive or nonocclusive. RV can be a major association of systemic diseases like Behcet's disease, sarcoidosis and systemic lupus erythematosus. Newer modalities, like ultra-widefield fundus fluorescein angiography, can help in the management of RV. Effective treatment of noninfectious RV requires anti-inflammatory and immunosuppressive therapy. The patients may require treatment with high-dose corticosteroids and biological agents. Anti-vascular endothelial growth factor injections and laser photocoagulation may be indicated to treat the occlusive disease. Prompt treatment may prevent complications like vitreous hemorrhage, neovascular glaucoma, and tractional retinal detachment. The treatment more often requires a multidisciplinary approach. Key Messages: This review provides a comprehensive update on the various causes of noninfectious RV, including both systemic and isolated ocular conditions. It also details various complications and management strategies for this condition. [ABSTRACT FROM AUTHOR]

In this study, we explored inter-ocular asymmetry (between the two eyes of the same patient) using optical coherence tomography angiography (OCTA) in patients with diabetes mellitus (DM) at different retinopathy stages. A total of 258 patients were divided into four groups: no DM, DM without diabetic retinopathy (DR), non-proliferative DR (NPDR), and proliferative DR (PDR). Superficial and deep vessel density (SVD, DVD), superficial and deep perfusion density (SPD, DPD), foveal avascular zone (FAZ) area, perimeter and circularity were calculated, and asymmetry index (AI) was used to evaluate the asymmetry of two eyes of the same subject. AIs of SPD, SVD, FAZ area and FAZ perimeter in the PDR group were larger than all other 3 groups (all p

IntroductionFew studies have explored MultiColor™ imaging (MCI) in evaluating retinal vascular diseases, particularly branch retinal vein occlusion (BRVO). This study aimed to compare the identification of retinal vessel whitening in BRVO using MCI by scanning confocal laser versus conventional white-flash color fundus photography (CFP).MethodsPaired images of consecutive patients diagnosed with BRVO who underwent same-day MCI and CFP were reviewed. Visualization of vessel whitening on MCI and CFP was graded and scored using a scale by two masked graders. A longitudinal analysis of the vessel grading score was performed to evaluate the vessel whitening detection by MCI. A correlation analysis was conducted between vessel whitening on MCI and the measured area of retinal ischemia on fluorescein angiography to evaluate the MCI performance.ResultsForty-four eyes of 41 patients (mean age 69 ± 14 years; 61% female) were analyzed. MCI demonstrated superior vessel whitening visibility score than CFP (p < 0.001). Longitudinal analysis showed no significant changes in vessel whitening visibility scores over a mean follow-up time of 430 ± 648 days (p = 0.655). There was a significantly positive correlation between the grading score of vessels whitening by MCI and the area of ischemia by fluorescein angiography (r2 = 0.15; p = 0.036).ConclusionMCI appears to provide a superior detection of whitening BRVO compared to CFP, serving as a rapid and non-invasive correlate of retinal ischemia.

BackgroundCompared to standard neuro-diagnostic techniques, retinal biomarkers provide a probable low-cost and non-invasive alternative for early Alzheimer's disease (AD) risk screening. We have previously quantified the periarteriole and perivenule capillary free zones (mid-peripheral CFZs) in cognitively unimpaired (CU) young and older adults as novel metrics of retinal tissue oxygenation. There is a breakdown of the inner retinal blood barrier, pericyte loss, and capillary non-perfusion or dropout in AD leading to potential enlargement of the mid-peripheral CFZs. We hypothesized the mid-peripheral CFZs will be enlarged in CU older adults at high risk for AD compared to low-risk individuals.Methods20 × 20° optical coherence tomography angiography images consisting of 512 b-scans, 512 A-scans per b-scan, 12-µm spacing between b-scans, and 5 frames averaged per each b-scan location of the central fovea and of paired major arterioles and venules with their surrounding capillaries inferior to the fovea of 57 eyes of 37 CU low-risk (mean age: 66 years) and 50 eyes of 38 CU high-risk older adults (mean age: 64 years; p = 0.24) were involved in this study. High-risk participants were defined as having at least one APOE e4 allele and a positive first-degree family history of AD while low-risk participants had neither of the two criteria. All participants had Montreal Cognitive Assessment scores ≥ 26. The mid-peripheral CFZs were computed in MATLAB and compared between the two groups.ResultsThe periarteriole CFZ of the high-risk group (75.8 ± 9.19 µm) was significantly larger than that of the low-risk group (71.3 ± 7.07 µm), p = 0.005, Cohen's d = 0.55. The perivenule CFZ of the high-risk group (60.4 ± 8.55 µm) was also significantly larger than that of the low-risk group (57.3 ± 6.40 µm), p = 0.034, Cohen's d = 0.42. There were no significant differences in foveal avascular zone (FAZ) size, FAZ effective diameter, and vessel density between the two groups, all p > 0.05.ConclusionsOur results show larger mid-peripheral CFZs in CU older adults at high risk for AD, with the potential for the periarteriole CFZ to serve as a novel retinal vascular biomarker for early AD risk detection.