Internal Carotid Artery Stenosis and Ipsilateral Subretinal Drusenoid Deposits.

Purpose: Subretinal drusenoid deposits (SDDs) in age-related macular degeneration (AMD) are strongly associated with vasculopathies such as myocardial infarction and ischemic stroke. This study evaluates ischemic stroke subjects for SDDs to determine whether ocular hypoperfusion from internal carotid artery (ICA) stenosis is associated with ipsilateral SDDs.

Methods: A cross-sectional study at Mount Sinai Hospital recruited 39 subjects with ischemic stroke (aged 52-90; 18 women, 21 men); 28 completed all study procedures.

SUBRETINAL DRUSENOID DEPOSITS AND SOFT DRUSEN: Are They Markers for Distinct Retinal Diseases?

Purpose: Soft drusen and subretinal drusenoid deposits (SDDs) characterize two pathways to advanced age-related macular degeneration (AMD), with distinct genetic risks, serum risks, and associated systemic diseases.

Methods: One hundred and twenty-six subjects with AMD were classified as SDD (with or without soft drusen) or non-SDD (drusen only) by retinal imaging, with serum risks, genetic testing, and histories of cardiovascular disease (CVD) and stroke.

Results: There were 62 subjects with SDD and 64 non-SDD subjects, of whom 51 had CVD or stroke.

Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years.

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings.

Background: Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases.

An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging.

Results: The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.

Automated diabetic retinopathy screening for primary care settings using deep learning.

Diabetic Retinopathy (DR) is one of the leading causes of blindness in the United States and other high-income countries. Early detection is key to prevention, which could be achieved effectively with a fully automated screening tool performing well on clinically relevant measures in primary care settings. We have built an artificial intelligence-based tool on a cloud-based platform for large-scale screening of DR as referable or non-referable.

Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Purpose: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years.

Methods: The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study.

A New and Improved Method for Automated Screening of Age-Related Macular Degeneration Using Ensemble Deep Neural Networks.

In this paper, we provide a new framework on deep learning based automated screening method for finding individuals at risk of developing Age-related Macular Degeneration (AMD). We studied the appropriateness of using the transfer learning to screen AMD by using color fundus images. We make use of the Age-Related Eye Disease Study (AREDS) dataset with nearly 150,000 images, which also provided qualitative grading information by expert graders and ophthalmologists.