Reti-Eye

A retinal-based AI diagnostic solution for glaucoma, cataracts, and other retinal diseases.

Reti-Eye independently checks your retinal photographs and provides decision support on whether to refer to an ophthalmologist

Features

High Accuracy in detecting eye disease

Our algorithms are extensively trained with high-quality data and validated rigorously by leading medical experts. We ensure accuracy of 92% sensitivity and 95% specificity.

Immediate results at the point of care

We autonomously detect signs of disease and give you a report in less than a minute on-site to provide a decision on needs before visiting an eye specialist.

Fully automated diagnosis

Retinal diseases – diabetic retinopathy, macular degeneration, epiretinal membranes, etc.

Opacities of the optical media – cataracts, vitreous opacity, vitreous hemorrhage

Glaucoma – glaucomatous optic disc, Retinal Nerve Fiber Layer defect, disc hemorrhage

Detection of referable eye diseases

Reti-Eye automatically detects referable eye diseases including retinal abnormality, glaucoma, and media opacity issues by taking a single retina photo. Reti-Eye recommends patients seek an opthalmologist’s consultation for clinical diagnosis if issues are found.

Normal retina

Retinal abnormality

Glaucoma

Media opacity

Common irreversible eye diseases

The leading causes of blindness and low vision are primarily age-related eye diseases such as age-related macular degeneration, cataract, diabetic retinopathy, and glaucoma. 

Glaucoma

Glaucoma is a disease that damages the optic nerve in your eyes. Often known as a silent killer, if left untreated, it will lead to permanent vision loss.

Age-related Macular Degeneration

Age-related Macular Degeneration (AMD) is an age-related deterioration of the vision focal point in your eye. AMD can interfere with daily activities such as driving, reading, and writing.

Cataract

A cataract is a clouding of the lens in the eye that affect vision. Most cataracts are related to aging and are commonly found among older people.

Diabetic Retinopathy

Diabetic Retinopathy (DR) is a common complication of diabetes leading to severe and permanent blindness. As DR is asymptomatic in the early stages, patients may not notice any change in their vision at first.

Benefits

Affordable

Save costs through early detection and management of glaucoma, cataracts, and other retinal diseases.

Accesible

Screen for eye diseases in all healthcare settings before your long-awaited appointment with an ophthalmologist.

Preventive

Know your risk for referable eye disease in time without unnecessary referrals. The earliest possible detection allows the earliest possible treatment.

How Reti-Eye works

Eye Scan

Take 1 retinal photograph per eye using a fundus camera by optometrists or technicians

Upload

Submit images to the cloud for analysis and type patient information

Analysis

Automatically analyze for signs of eye disease risk within a minute

Report

Generate personalized health screening results and download the report

Referral

Refer to specialists for further follow-up or suggest lifestyle changes and regular check-ups

7

Get your result within a minute

Approval of Medical device & Commercial use

Approved in 8 territories

Mediwhale is the only company that obtained approval for eye disease risk assessment using retinal images as a biomarker. Reti-Eye has approvals for the medical device in the EU and Asia and is under FDA clearance in the US.

DrNoon is an approved product name of Reti-Eye & Reti-CVD in some territories.

Publication

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

PLOS ONE

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method.

Abstract

Deep learning is effective for classifying non-referable versus referable eye conditions using fundus photographs

Investigative Ophthalmology & Visual Science

Fundus photographs is the most common imaging modality for screening eye disease. This study aimed to determine whether deep learning could be utilized to distinguish referable eye disease (RED) from normal fundus photographs for general eye screening.