Eye Images Research Articles

System and method to diagnose conjunctivitis in the eye of a user

This Proposed work explores how machine learning can be used to diagnose conjunctivitis, a common eye ailment. The main goal of the study is to capture eye images using camera-based systems, perform image pre-processing, and employ image segmentation techniques, particularly the UNet++ and U-net models. Additionally, the study involves extracting features from the relevant areas within the segmented images and using Convolutional Neural Networks for classification. All this is carried out using TensorFlow, a well-known machine-learning platform. The research involves thorough training and assessment of both the UNet and U-net++ segmentation models. A comprehensive analysis is conducted, focusing on their accuracy and performance. The study goes further to evaluate these models using both the UBIRIS dataset and a custom dataset created for this specific research. The experimental results emphasize a substantial improvement in the quality of segmentation achieved by the U-net++ model, the model achieved an overall accuracy of 97.07. Furthermore, the UNet++ architecture displays better accuracy in comparison to the traditional U-net model. These outcomes highlight the potential of U-net++ as a valuable advancement in the field of machine learning-based conjunctivitis diagnosis.

A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

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.

Feature decomposition-based gaze estimation with auxiliary head pose regression

Recognition and understanding of facial images or eye images are critical for eye tracking. Recent studies have shown that the simultaneous use of facial and eye images can effectively lower gaze errors. However, these methods typically consider facial and eye images as two unrelated inputs, without taking into account their distinct representational abilities at the feature level. Additionally, implicitly learned head pose from highly coupled facial features would make the trained model less interpretable and prone to the gaze-head overfitting problem. To address these issues, we propose a method that aims to enhance task-relevant features while suppressing other noises by leveraging feature decomposition. We disentangle eye-related features from the facial image via a projection module and further make them distinctive with an attention-based head pose regression task, which could enhance the representation of gaze-related features and make the model less susceptible to task-irrelevant features. After that, the mutually separated eye features and head pose are recombined to achieve more accurate gaze estimation. Experimental results demonstrate that our method achieves state-of-the-art performance, with an estimation error of 3.90° on the MPIIGaze dataset and 5.15° error on the EyeDiap dataset, respectively.

Multimodal Eye Imaging, Retina Characteristics, and Psychological Assessment Dataset

The eyes provide insights into psychology, potentially offering a distinctive perspective for psychological health profiles. However, there exist a notable deficiency in datasets that simultaneously encompass eye features and psychological assessments. To address this gap, our study presents a dataset that included Fundus Photography, Psychological Assessment, Retina Characteristics, and Multimodal Imaging (FPRM). FPRM dataset comprise fundus images at different wavelengths (548 nm and 605 nm), image of oxygen saturation for the retina and 8 specific retinal vessels, videos of retinal blood flow and pupillary light reflex, along with 61 items of multimodal quantitative measurement from 384 participants. Additionally, it features psychological assessments across five dimensions (geriatric depression, generalized anxiety disorder, insomnia, activities of daily living, and deterioration), accompanied by fundus photographs and 6 items of retina characteristics from 1683 participants. FPRM dataset is the first to integrate multimodal ophthalmic data and psychological assessments, not only advancing the development of machine learning applications but also facilitating in-depth research into the relationship between eye health and psychological health profiles.

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.

Advances in Cerebral Palsy Treatment

Background: Cerebral palsy is a complex neurodevelopmental disorder with various etiological factors and treatment options. This narrative review aimed to summarize the causes of cerebral palsy, identify areas needing additional research in treatment approaches, and highlight areas requiring further investigation. In order to provide a thorough overview of management techniques to lessen the effects of the illness and its consequences, this review has drawn data from a number of studies. Introduction: Prematurity increases the risk of brain damage during the developing stage and accounts for a sizable fraction of cerebral palsy cases. In a sizable portion of cases, maternal diabetes and hypertension are listed as the main causes. Damage to the brain tissue results from hypoxic-ischemic injuries sustained during pregnancy that upset the equilibrium of oxidants and antioxidants. To alter the oxidative stress pathway and ease related issues, pharmacological treatments, such as therapeutic hypothermia, free radical inhibition therapy, and mitochondrial therapy, have been proposed. Therapeutic strategies, such as physiotherapy, occupational therapy, speech therapy, and surgical interventions, have added quality to the lives of the children. Some of the most recent developments in this area include the development of biomarkers for muscle activity detection, machine learning to predict the types of cerebral palsy and abnormal movements, disease prediction with eye images, wireless inertia measuring unit for spasticity detection, computerbased video analysis of typical and atypical infants, identification of intellectual disabilities with algorithms, and deep learning methods for predicting cerebral palsy. Methods: This narrative review is based on a careful analysis of numerous researches conducted on cerebral palsy, which have served as the basis for statistical distribution. It reviews the causes of cerebral palsy, available treatments, and ongoing research with the goal of providing physicians and researchers in the field with useful information. The objectives, study questions, inclusion criteria, and search approach have all been outlined in a thorough protocol. To find pertinent research published up to September 2021, a literature search was carried out using electronic databases, including Google Scholar, PubMed, Cochrane Library, Scopus, and Web of Science. A combination of pertinent keywords, such as "cerebral palsy," "management," "technology," "wearable technology," "prematurity," and "artificial intelligence," has been used in the search approach. Results: Recent advances in the field include the discovery of biomarkers for the detection of muscle activity, machine learning algorithms to predict the types of cerebral palsy and abnormal movements, disease prediction using eye images, wireless inertia measuring units for the detection of spasticity, computer-based video analysis for the detection of atypical infants, and algorithms to identify intellectual disabilities. Additionally, employing technologies, like virtual reality systems, electrical stimulators, activity trackers, machine learning, and deep learning approaches, has shown promise in evaluating, diagnosing, and predicting treatment outcomes linked to gait, upper limb, and lower limb function. Conclusion: Future research should examine the clinical application of nanomedicine, stem cell therapy, and cutting-edge therapeutic strategies to prevent hypoxic-ischemic damage in the developing brain. Additionally, research is required to effectively assist children with severe speech difficulties using alternate communication modalities and cutting-edge computational tools. The outcomes for people with cerebral palsy can be improved by combining interdisciplinary efforts with cutting-edge technological interventions.

Development and Testing of Artificial Intelligence-Based Mobile Application to Achieve Cataract Backlog-Free Status in Uttar Pradesh, India

BackgroundUttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app “Roshni” for cataract screening. MethodThe AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm’s suitability for field use. ResultsThe cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %. ConclusionThis paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.

Comparison of Scheimpflug Imaging (Pentacam HR) and Swept-Source Optical Coherence Tomography (CASIA2) in Eyes With Macular Corneal Dystrophy.

Assessment of tomographic characteristics and interdevice comparability between Scheimpflug imaging (Pentacam HR, Oculus Optikgeräte GmbH, Wetzlar, Germany) and swept-source optical coherence tomography (CASIA2, Tomey Corp., Nagoya, Japan) in eyes with macular corneal dystrophy (MCD). Eyes with MCD were examined by Pentacam HR and CASIA2. Interdevice comparison was performed using a Wilcoxon matched pairs test and Bland-Altman plots with 95% limit of agreement. A Spearman rank correlation coefficient was used for correlating indices of both devices. This retrospective study included 31 eyes of 18 patients (mean age: 32.1 ± 10.7 years). Eyes with MCD demonstrated a moderate astigmatism with a Cylinder anterior of 2.56 ± 1.50 D (Pentacam HR) and 2.52 ± 1.57 D (CASIA2) without a difference between both devices. CASIA2 (0.34 ± 0.14 D) measured lower values of Cylinder posterior compared with Pentacam HR (0.96 ± 0.66 D) (P < 0.0001). Comparison of pachymetry (Pentacam HR vs. CASIA2) showed higher values of the central corneal thickness (619 ± 227 μm vs. 445 ± 67 μm, P = 0.0001) and the thinnest corneal thickness (499 ± 165 μm vs. 430 ± 60 μm, P = 0.0167) for Pentacam HR. Corneal densitometry measurement revealed that increasing gray scale units caused a greater interdevice difference for pachymetry values, as Pentacam HR measured higher than CASIA2 for more opaque corneas. Eyes with MCD tend to have thinner corneas and a higher amount of corneal astigmatism than healthy eyes. In advanced MCD, Scheimpflug technology may mistakenly overestimate corneal thickness. The pachymetry measurement of the optical coherence tomography should be used when planning corneal surgery such as excimer laser-assisted phototherapeutic keratectomy to determine the ablation depth.

Comparison between indocyanine green angiography and optical coherence tomography angiography for the detection of nonexudative macular neovascularization.

Patients with unilateral treatment-naïve exudative neovascular age-related macular degeneration (nAMD) were examined for the presence of nonexudative macular neovascularization (neMNV) in their fellow eye using a multimodal imaging approach. This is intended to determine the sensitivity of individual imaging methods, namely, indocyanine green angiography (ICGA) and optical coherence tomography angiography (OCTA). In this retrospective cross-sectional study, ICGA images of the nonexudative eye were analyzed for the presence of plaques. SS-OCTA outer retinal segmentations were evaluated for neovascular flow in en-face scans and/or color-coded flow in B-scans. The findings were matched with a double-layer sign (DLS) or pigment epithelium detachment (PED) on conventional OCT. In total, neMNV was diagnosed in the fellow eye of 40 of 241 patients (17%) using a multimodal imaging approach employing both ICGA and SS-OCTA. 25 eyes (10%) showed neMNV in both modalities, while 7 (3%) were only detected by ICGA and 8 (3%) by OCTA alone. The sensitivities of ICGA therefore were 80% (32/40) and 83% (33/40) for OCTA. Of the 40 eyes with neMNV, OCT revealed DLS in 25/40 (63%) and PED in 17/40 (43%) of the cases. None of the modalities alone could detect all neMNV in the partner eye of Caucasians with unilateral treatment-naïve exudative nAMD. ICGA and OCTA showed comparable sensitivity. The combination of ICGA, OCTA, and OCT provides the most comprehensive screening for this AMD subtype.

Effects of weaning and inactivated Lactobacillus helveticus supplementation on dairy calf behavioral and physiological indicators of affective state

The objectives of this study were to determine if weaning would induce behavioral and physiological indicators of a negative affective state, and if supplementation of inactivated Lactobacillus helveticus (ILH) to dairy calves would reduce those indicators of negative affect during weaning. Male Holstein calves (n = 23) were enrolled in the study on d 1 of life. The calves were housed in individual pens in 1 of 4 rooms for the 42 d study. Calves began a stepdown weaning from 9 L/d of milk replacer (MR), at 150 g of MR powder/L, on d 35 and received 6 L/d on d 35 - 36, 3 L/d on d 37 - 38, and 0.4 L/d on d 39 - 42. The MR was divided between 3 meals/d until the last 0.4 L/d phase which was divided between 2 meals/d. Calves had ad libitum water access throughout the study and calf starter from d 28 onwards. Within room, calves were assigned to 1 of 2 treatments: 1) control (CON; n = 11) and 2) 5 g of ILH/d split over and mixed into the 0800 h and 2000 h milk feedings from d 3-42 (ILH; n = 12). Lying behavior was recorded using HOBO data loggers from d 21-41. On d 33, 37 and 41, infrared eye images were taken to determine maximum eye temperature (MET), saliva samples were collected to determine cortisol concentration, and play assessments were conducted to quantify play behavior. On d 34, 38, and 42, blood samples were collected to determine blood serotonin concentration, whereas on d 38 and 39, calves were tested with a cognitive task. A subset of calves (n = 5/treatment) were euthanized to collect gut and brain tissue samples for serotonin concentration on d 43. Weaning resulted in fewer (d 37-41, tendency: d 36), but longer (d 38-41, tendency: d 37), lying bouts and reduced play (d 41), although no changes in lying time, MET, saliva cortisol, nor blood serotonin were detected with initiation of weaning. Supplementation of ILH was associated with lower lying time throughout the study, and reduced play duration and higher salivary cortisol and MET during weaning. No differences in lying bouts, play count, blood and tissue (colon, ileum, prefrontal cortex and brain stem) serotonin concentration, and time to complete the cognitive task were detected between the treatments. Overall, weaning induced behavioral changes indicative of negative affective state, and some behavioral differences were observed with ILH supplementation both before and during weaning, with some physiological changes observed during weaning.

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images

The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We collected a dataset composed of 442 OCT images on which we annotated 6847 HE and the presence of DRIL. A complex operational pipeline was defined to implement data cleaning and image transformations, and train two DL models. The state-of-the-art neural network architectures (Yolov7, ConvNeXt, RegNetX) and advanced techniques were exploited to aggregate the results (Ensemble learning, Edge detection) and obtain a final model. The DL approach reached good performance in detecting HE and classifying DRIL. Regarding HE detection the model got an AP@0.5 score equal to 34.4% with Precision of 48.7% and Recall of 43.1%; while for DRIL classification an Accuracy of 91.1% with Sensitivity and Specificity both of 91.1% and AUC and AUPR values equal to 91% were obtained. The P-value was lower than 0.05 and the Kappa coefficient was 0.82. The DL models proved to be able to identify HE and DRIL in eyes with DME with a very good accuracy and all the metrics calculated confirmed the system performance. Our DL approach demonstrated to be a good candidate as a supporting tool for ophthalmologists in OCT images analysis.

Comparative Study of Classification of Eye Disease Types Using DenseNet and EfficientNetB3

The purpose of this research is to build a classification model that can perform the eye disease identification process so that the diagnosis of eye disease can be known and medical action can be taken as early as possible. This research used a dataset which has a total of 4217 eye image data and had 4 main classes namely cataract, diabetic retinopathy, glaucoma, and normal. With the data distribution of 1038 cataract images, 1098 diabetic retinopathy images, 1007 glaucoma images, and 1074 normal images, which of this data will be divided with a data percentage scheme of 50:10:40, 60:10:30, and 70:10:20, to see the results of which dataset division can produce optimal accuracy. In this study, the classification process will use 2 CNN transfer learning architectures, namely DenseNet, and efficientnetb3, which are both trained using the ImagiNet dataset. The results obtained after completing the testing process on the model built using the DenseNet architecture get optimal accuracy when using data division as much as 60:10:30, which is 78.59% while using the efficientnetb3 architecture optimal accuracy results when using the data division of 70:10:20, which is 95.66%. In research on the classification that had previously been done, it is very rare to find a classification process for eye disease types, therefore, in this study, the classification process will be carried out and provide an overview of the eye disease classification process with the CNN transfer learning method with more optimal accuracy results.

Optical Glaucoma Detection using Deep Learning And streamlit

The Project “Glaucoma is a disease that relates to the vision of the human eye”. This disease is considered as the irreversible disease that results in the vision deterioration. Much deep learning (DL) models have been developed for the proper detection of glaucoma so far. So this paper presents architecture for the proper glaucoma detection based on the deep learning by making use of the convolutional neural network (CNN). The differentiation between the patterns formed for glaucoma and non-glaucoma can find out with the use of the CNN. The CNN provides a hierarchical structure of the images for differentiation. Proposed work can be evaluated with a total of six layers. Here the dropout mechanism is also used for achieving the adequate performance in the glaucoma detection. The datasets used for the experiments are the SCES and ORIGA. Glaucoma is a group of related eye disorders that cause damage to the optic nerve that carries information from the eye to the brain which can get worse over time and lead to blindness. It is very important that glaucoma is detected as early as possible for proper treatment. In this paper, we have proposed a Convolutional Neural Network (CNN) system for early detection of Glaucoma. Initially, eye images are augmented to generate data for Deep learning. The eye images are then pre- processed to remove noise using Gaussian Blur technique and make the image suitable for further processing. The system is trained using the pre-processed images and when new input images are given to the system it classifies them as normal eye or glaucoma eye based on the features extracted during training..

Cascaded learning with transformer for simultaneous eye landmark, eye state and gaze estimation

Eye tracking have garnered attention in human–machine interaction, disease monitoring, biometrics, etc. Existing investigations for eye tracking have predominantly concentrated on individual task for pupil detection or gaze estimation, overlooking the implicit relationships that exist among different tasks for eye tracking. In this work, we introduce a cascaded framework with transformer to collaboratively realize eye landmark detection, eye state detection and gaze estimation. Within our framework, we leverage Transformer to capture long dependencies with explicit eye-related structural information and implicit correlation among different tasks. Furthermore, the proposed cascade iteration framework alternatively optimize each task and boost the overall performance for pupil center, eye state and gaze estimation simultaneously. To address the problem of manual annotation, we further introduce the Control-Eye Diffusion Model (CEDM), a controllable eye image generation method conditioned on a simple contour with structure information. The proposed methods are evaluated on challenging datasets such as GI4E, BioID and MPIIGaze, and the results show that our methods outperform state-of-the-art methods in several tasks.

Optic nerve head and retinal nerve fiber layer changes by spectral domain optical coherence tomography in glaucoma suspects at tertiary care hospital

Introduction: Glaucoma is a progressive optic neuropathy characterized by functional loss or optic nerve dysfunction which is one of the leading causes of irreversible but preventable blindness. The aim of the study was to determine which of the retinal nerve fiber layer and optic nerve head parameters are reliable markers of optic nerve damage in glaucoma suspects. Methods: Observational cross-sectional study of 250 patient’s including147 glaucoma suspect eyes, 103 normal eyes. All subjects underwent complete eye examination and imaging with the spectral domain optical coherence tomography (SD-OCT) cirrus-TM OCT (Carl Zeiss Meditec, Dublin, CA). Retinal nerve fiber layer (RNFL) and optic nerve head (ONH) OCT protocols were used to evaluate all study participants. The main outcome measures were the difference in OCT parameters among groups. Results: Study shows statistically significant difference in the average RNFL thickness, superior, nasal, inferior, temporal quadrants, rim area, cup volume, average cup to disc ratio (CDR), vertical CDR with P value &lt;0.001 between the two groups. Average CDR, vertical CDR and cup volume had significantly greater AROC values (ROC: 0.99, 0.98, 0.95) than RNFL parameters for discriminating glaucoma suspects from normal eyes. Conclusion: Assessment of RNFL, macular and ONH damage with SD-OCT has been proven useful for diagnosing the disease at different levels of severity, as well as for quantifying risk in glaucoma suspects. In our study ONH measurements, as provided by the SD OCT, have more diagnostic value than RNFL parameters in the diagnosis of glaucoma suspects from normal. Keywords: glaucoma suspects; optic nerve head; retinal nerve fiber layer; optical coherence tomography

Blue Wavelength of Scanning Laser Ophthalmoscope Potentially Detects Arteriosclerotic Lesions in Diabetic Retinopathy.

(1) Background: The fundus examination is one of the best and popular methods in the assessment of vascular status in the human body. Direct viewing of retinal vessels by ophthalmoscopy has been utilized in judging hypertensive change or arteriosclerosis. Recently, fundus imaging with the non-mydriatic scanning laser ophthalmoscope (SLO) has been widely used in ophthalmological clinics since it has multimodal functions for optical coherence tomography or angiography with contrast agent dye. The purpose of this study was to examine the utility in detecting arteriosclerosis of retinal vessels in SLO images; (2) Methods: Both color and blue standard field SLO images of eyes with diabetic retinopathy (DR) were examined retrospectively. Retinal arteriosclerosis in color SLO images was graded according to the Scheie classification. Additionally, characteristics of retinal arterioles in blue SLO images were identified and examined for their relevance to arteriosclerosis grades, stages of DR or general complications; (3) Results: Relative to color fundus images, blue SLO images showed distinct hyper-reflective retinal arterioles against a monotone background. Irregularities of retinal arterioles identified in blue SLO images were frequently observed in the eyes of patients with severe arteriosclerosis (Grade 3: 79.0% and Grade 4: 81.8%). Furthermore, the findings on arterioles were more frequently associated with the eyes of DR patients with renal dysfunction (p < 0.05); (4) Conclusions: While color SLO images are equally as useful in assessing retinal arteriosclerosis as photography or ophthalmoscopy, the corresponding blue SLO images show arteriosclerotic lesions with high contrast in a monotone background. Retinal arteriosclerosis in eyes of advanced grades or advanced DR frequently show irregularities of retinal arterioles in the blue images. The findings of low, uneven, or discontinuous attenuation were easier to find in blue than in color SLO images. Consequently, blue SLO images can show pathological micro-sclerosis in retinal arterioles and are potentially one of the safe and practical methods for the vascular assessment of diabetic patients.

Nineteen Eighty-Four in 1974: the surveillance imaginary of the New Mexico Civil Liberties Union Campaign for privacy awareness

ABSTRACT This article maps the surveillance imaginary of a unique public awareness initiative by the New Mexico Civil Liberties Union and Godfrey Reggio’s Institute for Regional Education in 1974 that consisted of two parts: a situationist-inspired campaign using and surrealistically subverting popular media to raise awareness of surveillance, paired with a newspaper supplement documenting detailed intrusions on the privacy and liberty of citizens on a number of fronts, representing an encroaching society of social control. Taken as a whole, the surveillance imaginary of the campaign combined images of eyes, computers, and rats in mazes with detailed discussions of databases, militarized policing, and direct behavioral control justified by stories of rising violence in society to critique both state surveillance and the growing corporatization of the public sphere.

Single-line macular optic coherence tomography to confirm optic neuropathies in awake infants and young children

BackgroundHandheld optical coherence tomography (HH-OCT) can image awake, young children but lacks integrated segmentation/analysis software. OCT imaging of eyes with optic neuropathies demonstrates ganglion cell layer (GCL) and ganglion cell complex (GCC) thinning, with a normal or thickened inner nuclear layer (INL). We compared pediatric normative data with GCL/INL and GCC/INL ratios from HH-OCT macular scans of awake young children with clinically diagnosed optic neuropathies. MethodsMacular HH-OCT from awake children with optic neuropathies was prospectively obtained using Bioptigen (Leica Microsystems, Wetzlar, Germany). The GCL, GCC, and INL were manually measured by two readers using ImageJ from single-line macular scans at the thickest points nasal and temporal to the fovea, respectively, and the GCL/INL and GCC/INL ratios were calculated and compared with normative data. ResultsHH-OCT images from 17 right eyes of 17 children (mean age, 4.3 ± 2.9 years) with optic neuropathies were analyzed. Mean nasal (17 eyes) and temporal (16 eyes) GCL/INL ratios with optic neuropathies were 0.44 ± 0.38 (95% CI, 0.26-0.62) and 0.26 ± 0.22 (95% CI, 0.15-0.36), respectively. Corresponding normative GCL/INL ratios are 1.26 ± 0.20 (95% CI, 1.19-1.34) and 1.23 ± 0.27 (95% CI, 1.13-1.33), respectively (P < 0.0001). Severe thinning precluded GCL measurements in 2 eyes nasally and 5 eyes temporally, resulting in GCL measurements of zero. Mean nasal (17 eyes) and temporal (16 eyes) GCC/INL ratios were 1.93 ± 0.70 (95% CI,1.60-2.27) and 1.67 ± 0.44 (95% CI,1.46-1.87). Corresponding normative ratios are 2.85 ± 0.38 (95% CI, 2.71-2.99) and 2.87 ± 0.42 (95% CI, 2.70-3.03), respectively (P < 0.0001). ConclusionsGCL/INL and GCC/INL ratios calculated from single-line macular HH-OCT scans in awake young children with optic neuropathies differ significantly from normative values and may thus have utility in helping to establish a diagnosis of optic neuropathy.

Evaluating the Diagnostic Accuracy of a Portable, Motorized, and Remotely Controlled Slit Lamp Imaging Adaptor Prototype for Head-Mounted Displays.

The purpose of this study was to validate the performance of a portable and remotely controlled slit lamp imaging adaptor. Twenty patients with anterior eye segment conditions participated in a randomized masked clinical trial. Imaging was performed using a Haag-Streit AG, BX 900 slit lamp biomicroscope and a new slit lamp prototype. Three ophthalmologists independently reviewed masked and randomized 2D images from both instruments and conducted physical eye examinations based on these images. Inter- and intra-grader reliability were assessed using kappa statistics, and sensitivity and specificity were determined with reference to the clinical eye examinations performed during the patients' visits. The sensitivity and specificity of the evaluations with the prototype were 47.8% and 64.1%. Similarly, the evaluations from the conventional system obtained a sensitivity and specificity of 49.5% and 66.2%. The differences in the sensitivity and specificity between imaging modalities were not statistically significant (P > 0.05). The intra-grader reliability showed moderate to substantial agreement between the systems (κ = 0.522-0.708). The inter-grader reliability also indicated moderate agreement for the evaluations with the conventional system (κ = 0.552) and the prototype (κ = 0.474). This study presents a new prototype that exhibits diagnostic accuracy on par with conventional slit lamps and moderate reliability. Further studies with larger sample sizes are required to characterize the prototype's performance. However, its remote functionality and accessibility suggest the potential to extend eye care. The development of portable and remotely controlled eye imaging systems will enhance teleophthalmology services and broaden access to eye care at the primary care level.

Design of A Cataract Detection System based on The Convolutional Neural Network

Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.