- Dr. Noon Fundus_Korea MFDS : [Product Name] DrNoon for fundus screening / [Product License No.] 제허 20-618호 / [Date of Initial Approval] 8 October 2020 / [Validity Period] 8 October 2020 – 30 November 2030 / [Issuer] The Ministry of Food and Drug Safety
- Dr. Noon Fundus_CE : EC Certificate issued under the European Medical Device Regulation (EU MDD) / [EC Certificate No.] KR21/81826550 / [First Issue Date] 11 May 2021 / [Expiry Date] 31 December 2028 / [Issuer] SGS Belgium NV
AI-powered Solution for
Diagnosing Retinal Abnormalities
Dr. Noon Fundus is a retina-based AI diagnostic software that diagnoses retinal abnormalities.
By analyzing a single retinal photograph, it automatically detects four types of retinal abnormalities: Glaucoma, Media Opacity, Age-related Macular Degeneration, Diabetic Retinopathy, Dr. Noon Fundus assists ophthalmologists, primary care physicians, and screening centers in making
diagnoses related to retinal abnormalities
Retinal Abnormalities Detectable
with Dr. Noon Fundus
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Glaucoma (Suspect)
Glaucoma is a disease that damages the optic nerve in the eyes.
Often known as a silent killer,if left untreated, it will lead to permanent vision loss.*Source - National Eye Institute, National Institutes of Health
AI Detected Glaucoma (Suspect) from the Images
Photos of Fundus (Left / Right)
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Media Opacity
A Media Opacity is a clouding of the lens in the eye that affects vision. Most Media Opacity are related to aging and are commonly found among older people.
*Source - National Eye Institute, National Institutes of Health
AI Detected Media Opacity from the Images
Photos of Fundus (Left / Right)
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Age-related Macular Degeneration (AMD)
Age-related Macular Degeneration (AMD) is an age-related deterioration of the vision focal point in your eye. AMD can interfere with daily activities
*Dr. Noon Fundus screens for age-related macular degeneration and the report flags it as retina normal or abnormal.
such as driving, reading, and writing.*Source - National Eye Institute, National Institutes of Health
AI Detected Age-related Macular Degeneration from the Images
Photos of Fundus (Left / Right)
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Diabetic Retinopathy (DR)
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.
*Dr. Noon Fundus screens for Diabetic Retinopathy (DR) and the report flags it as retina normal or abnormal.*Source - National Eye Institute, National Institutes of Health
AI Detected Diabetic Retinopathy from the Images
Photos of Fundus (Left / Right)
Patient Journey
Follow the step-by-step journey of a patient visiting the hospital
and see how they meet Dr. Noon Fundus.
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STEP 1Eye Scan
Take one picture of each eye to capture the retina.
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STEP 2Image Upload
The retinal images are uploaded to the healthcare institution’s server or cloud platform.
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STEP 3AI Analysis
Artificial intelligence automatically analyzes suspected retinal abnormalities.
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STEP 4Result Review
Retinal abnormalities are determined through a promptly delivered report.
Key Benefits
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High Accuracy
Based on learning from more than 100,000 retinal images verified by ophthalmology experts, we provide highly reliable diagnoses.
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Fast Test Results
The testing procedure is the same as existing retinal exams, and results are also delivered promptly.
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High Accessibility
Can be utilized in clinics, hospitals, and health screening centers, providing more patients with the opportunity to receive cardiovascular assessments.
Our Customers
Dr. Noon Fundus is utilized by leading hospitals,
clinics, and check-up centers in Korea and worldwide.
Publications & Webinars
Check out the clinical validations and related research papers that demonstrate
the accuracy, safety, and real-world effectiveness of Dr. Noon Fundus.
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Original Paper
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
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Conference Abstract
Deep Learning Is Effective for Classifying Non-referable versus Referable Eye Condition using Fundus Photographs
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Original Paper
Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma