# Retinal Imaging and Analysis **Type:** Topics **Canonical URL:** https://scholariq.org/topics/retinal-imaging-and-analysis/ ## Facts | Field | Value | | --- | --- | | Description | This cluster of papers focuses on the development and validation of deep learning algorithms for the detection and management of retinal diseases, particularly diabetic retinopathy. It includes topics such as vessel segmentation, optic nerve localization, cardiovascular risk prediction, macular degeneration, and glaucoma detection. | | Domain | Health Sciences | | Field | Medicine | | OpenAlex ID | t11438 | | Works | 369 | ## Topic papers all Showing 15 of 369. - [Optical Coherence Tomography](https://scholariq.org/papers/optical-coherence-tomography/) - [Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning](https://scholariq.org/papers/identifying-medical-diagnoses-and-treatable-diseases-by-image-based-deep/) - [Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045](https://scholariq.org/papers/global-prevalence-of-diabetic-retinopathy-and-projection-of-burden-through-2045/) - [A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants](https://scholariq.org/papers/a-large-genome-wide-association-study-of-age-related-macular-degeneration/) - [Deep learning-enabled medical computer vision](https://scholariq.org/papers/deep-learning-enabled-medical-computer-vision/) - [Age-Related Macular Degeneration: Etiology, Pathogenesis, and Therapeutic Strategies](https://scholariq.org/papers/age-related-macular-degeneration-etiology-pathogenesis-and-therapeutic/) - [Weighted Res-UNet for High-Quality Retina Vessel Segmentation](https://scholariq.org/papers/weighted-res-unet-for-high-quality-retina-vessel-segmentation/) - [Diabetic Retinopathy: A Position Statement by the American Diabetes Association](https://scholariq.org/papers/diabetic-retinopathy-a-position-statement-by-the-american-diabetes-association/) - [A foundation model for generalizable disease detection from retinal images](https://scholariq.org/papers/a-foundation-model-for-generalizable-disease-detection-from-retinal-images/) - [Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs](https://scholariq.org/papers/efficacy-of-a-deep-learning-system-for-detecting-glaucomatous-optic-neuropathy/) - [Seven new loci associated with age-related macular degeneration](https://scholariq.org/papers/seven-new-loci-associated-with-age-related-macular-degeneration/) - [Alternative treatments to inhibit VEGF in age-related choroidal neovascularisation: 2-year findings of the IVAN randomised controlled trial](https://scholariq.org/papers/alternative-treatments-to-inhibit-vegf-in-age-related-choroidal/) - [Choroidal vascularity index as a measure of vascular status of the choroid: Measurements in healthy eyes from a population-based study](https://scholariq.org/papers/choroidal-vascularity-index-as-a-measure-of-vascular-status-of-the-choroid/) - [Photocoagulation Treatment of Proliferative Diabetic Retinopathy: The Second Report of Diabetic Retinopathy Study Findings](https://scholariq.org/papers/photocoagulation-treatment-of-proliferative-diabetic-retinopathy-the-second/) - [Topography of diabetic macular edema with optical coherence tomography](https://scholariq.org/papers/topography-of-diabetic-macular-edema-with-optical-coherence-tomography/) ## Topic primary papers Showing 15 of 152. - [Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning](https://scholariq.org/papers/identifying-medical-diagnoses-and-treatable-diseases-by-image-based-deep/) - [Weighted Res-UNet for High-Quality Retina Vessel Segmentation](https://scholariq.org/papers/weighted-res-unet-for-high-quality-retina-vessel-segmentation/) - [A foundation model for generalizable disease detection from retinal images](https://scholariq.org/papers/a-foundation-model-for-generalizable-disease-detection-from-retinal-images/) - [Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs](https://scholariq.org/papers/efficacy-of-a-deep-learning-system-for-detecting-glaucomatous-optic-neuropathy/) - [A deep learning system for detecting diabetic retinopathy across the disease spectrum](https://scholariq.org/papers/a-deep-learning-system-for-detecting-diabetic-retinopathy-across-the-disease/) - [Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening](https://scholariq.org/papers/superpixel-classification-based-optic-disc-and-optic-cup-segmentation-for/) - [Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy](https://scholariq.org/papers/grader-variability-and-the-importance-of-reference-standards-for-evaluating/) - [DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field](https://scholariq.org/papers/deepvessel-retinal-vessel-segmentation-via-deep-learning-and-conditional-random/) - [Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy](https://scholariq.org/papers/using-a-deep-learning-algorithm-and-integrated-gradients-explanation-to-assist/) - [Glaucoma detection based on deep convolutional neural network](https://scholariq.org/papers/glaucoma-detection-based-on-deep-convolutional-neural-network/) - [ORIGA<sup>-light</sup>: An online retinal fundus image database for glaucoma analysis and research](https://scholariq.org/papers/origa-and-lt-sup-and-gt-light-and-lt-sup-and-gt-an-online-retinal-fundus-image/) - [CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging](https://scholariq.org/papers/cs-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-altimg-si1-svg-mml-msup/) - [IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge](https://scholariq.org/papers/idrid-diabetic-retinopathy-segmentation-and-grading-challenge/) - [Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks](https://scholariq.org/papers/automatic-detection-of-39-fundus-diseases-and-conditions-in-retinal-photographs/) - [Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images](https://scholariq.org/papers/deep-learning-models-for-the-detection-and-incidence-prediction-of-chronic/) --- Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.