Convolutional neural networks are constantly being developed, some efforts improve accuracy, some increase speed, and some increase accessibility. Improving accessibility allows the use of neural networks in a wider range of tasks, including the detection of eye diseases. Early diagnosis of eye diseases and visiting an ophthalmologist ca n prevent many vision disorders. Because of the importance of this issue, various data sets have been collected from the cornea of the eye to facilitate the process of making neural network models . However, m ost of the methods introduced in the past are computationally complicated . In this study, we tried to increase the accessibility of deep neural network models. We did this from the most basic level, i.e. changing and improving the c onvolutional layers. By doing so, we created a new general model that use our new convolutional layer named ArConv layers. Due to the proper functioning of the new layer, the model has suitable complexity for use in mobile phones and perform the task of diagnosing the presence of disease with high accuracy. The final model introduced by us has only 1.3 million parameters and compared to the MobileNetV2 model, which has 2.2 million parameters, after training the model only on the RfMiD data set under the same conditions, results showed that it had better accuracy in the final evaluation on the RfMiD test set. Keywords: Eye disease recognition, Deep convolutional neu ral networks, Machine learning, Computer aided diagnosis, Object detection. Vision is one of the most important senses in humans, according to the evolutionary characteristics of humans; vision is the largest system in brain and occupies 20 - 30% in of the cortex [1] . A s a result, it has a great impact on all aspects of life, including health, the ability to learn and work, help to others and its absence has bad consequences and severely affects people's lives. Eye diseases can cause vision disorders and blindness, and p eople who live in vulnerable communities have less access to medical diagnosis facilities, which will make the problem bigger.

Recent advancements in deep learning have shown significant potential for classifying retinal diseases using color fundus images. However, existing works predominantly rely exclusively on image data, lack interpretability in their diagnostic decisions, and treat medical professionals primarily as annotators for ground truth labeling. To fill this gap, we implement two key strategies: extracting interpretable concepts of retinal diseases using the knowledge base of GPT models and incorporating these concepts as a language component in prompt-learning to train vision-language (VL) models with both fundus images and their associated concepts. Our method not only improves retinal disease classification but also enriches few-shot and zero-shot detection (novel disease detection), while offering the added benefit of concept-based model interpretability. Our extensive evaluation across two diverse retinal fundus image datasets illustrates substantial performance gains in VL-model based few-shot methodologies through our concept integration approach, demonstrating an average improvement of approximately 5.8\% and 2.7\% mean average precision for 16-shot learning and zero-shot (novel class) detection respectively. Our method marks a pivotal step towards interpretable and efficient retinal disease recognition for real-world clinical applications.

Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervised methods, bringing in a heavy workload to biomedical staff and hence suffering in expanding effective databases. To address this issue, in this article, we established a label-free method, name 'SSVT',which can automatically analyze un-labeled fundus images and generate high evaluation accuracy of 97.0% of four main eye diseases based on six public datasets and two datasets collected by Beijing Tongren Hospital. The promising results showcased the effectiveness of the proposed unsupervised learning method, and the strong application potential in biomedical resource shortage regions to improve global eye health.

Eye diseases have posed significant challenges for decades, but advancements in technology have opened new avenues for their detection and treatment. Machine learning and deep learning algorithms have become instrumental in this domain, particularly when combined with Optical Coherent Technology (OCT) imaging. We propose a novel method for efficient detection of eye diseases from OCT images. Our technique enables the classification of patients into disease free (normal eyes) or affected by specific conditions such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), or Drusen. In this work, we introduce an end to end web application that utilizes machine learning and deep learning techniques for efficient eye disease prediction. The application allows patients to submit their raw OCT scanned images, which undergo segmentation using a trained custom UNet model. The segmented images are then fed into an ensemble model, comprising InceptionV3 and Xception networks, enhanced with a self attention layer. This self attention approach leverages the feature maps of individual models to achieve improved classification accuracy. The ensemble model's output is aggregated to predict and classify various eye diseases. Extensive experimentation and optimization have been conducted to ensure the application's efficiency and optimal performance. Our results demonstrate the effectiveness of the proposed approach in accurate eye disease prediction. The developed web application holds significant potential for early detection and timely intervention, thereby contributing to improved eye healthcare outcomes.

Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant clinical trajectories and detect lapses in care is critical to managing the disease and preventing blindness. Alas, much of the information necessary to support these goals is found only in the free text of the electronic medical record. To fill this information gap, we introduce a system for extracting evidence from clinical text of 19 clinical concepts related to diabetic eye disease and inferring relevant attributes for each. In developing this ophthalmology phenotyping system, we are also afforded a unique opportunity to evaluate the effectiveness of clinical language models at adapting to new clinical domains. Across multiple training paradigms, we find that BERT language models pretrained on out-of-distribution clinical data offer no significant improvement over BERT language models pretrained on non-clinical data for our domain. Our study tempers recent claims that language models pretrained on clinical data are necessary for clinical NLP tasks and highlights the importance of not treating clinical language data as a single homogeneous domain.

Since 2017, ophthalmology has been the busiest of all the medical specialties in the UK's National Health Service in terms of clinical appointments. Nearly 10 percent of all NHS outpatient appointments are related to eye problems. That's nearly 10 million appointments per year, and that number has risen by more than a third in the past five years. Between the ages of 18 and 65, the main cause of blindness is diabetic eye disease. But the population is getting older, and we're also seeing an increasing prevalence of diseases like age-related macular degeneration (AMD).

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

Scientists from the National Eye Institute (NEI) discovered five subpopulations of retinal pigment epithelium (RPE). Using artificial intelligence (AI), the researchers were able to analyze images of RPE at single-cell resolution to create a reference map that locates each subpopulation within the eye. Their findings are published in the journal Proceedings of the National Academy of Sciences, in a paper titled, "Single-cell–resolution map of human retinal pigment epithelium helps discover subpopulations with differential disease sensitivity." "These results provide a first-of-its-kind framework for understanding different RPE cell subpopulations and their vulnerability to retinal diseases, and for developing targeted therapies to treat them," said Michael F. Chiang, MD, director of the NEI, part of the National Institutes of Health. "The findings will help us develop more precise cell and gene therapies for specific degenerative eye diseases," said the study's lead investigator, Kapil Bharti, PhD, who directs the NEI Ocular and Stem Cell Translational Research Section.

Most of us think of tears as a human phenomenon, part of the complex fabric of human emotion. But they're not just for crying: All vertebrates, even reptiles and birds, have tears, which are critical for maintaining healthy eyesight. Now, a new study, published this week in the journal Frontiers in Veterinary Science, reveals that non-human animals' tears are not so different from our own. The chemical similarities are so great, in fact, that the composition of other species' tears--and how they're adapted to their environments--may provide insights into better treatments for human eye disease. Previously, scientists had studied closely only the tears of a handful of mammals, including humans, dogs, horses, camels, and monkeys.

A new eye test that uses artificial intelligence AI to study retina scans can predict age-related macular degeneration (AMD) three years before symptoms start. The first part of the'pioneering' test, developed by researchers at University College London, is called DARC. DARC involves injecting dye into a person's bloodstream to illuminate'stressed' endothelial cells in the retina, so they appear bright white under a fluorescent camera. These'stressed' retinal cells could lead to abnormalities and later leaking blood vessels – causing AMD, which can severely compromise the central field of vision. The second part of the test uses an AI algorithm, trained to detect whether the highlighted white spots are around the macula – which indicates high AMD risk.