- Dr. Noon CVD_Korea MFDS : [Product Name] DrNoon for CVD / [Product License No.] 제허 22-513호 / [Date of Initial Approval] 1 August 2022 / [Validity Period]1 August 2022 – 31 July 2027 [Issuer ] The Ministry of Food and Drug Safety
- Dr. Noon CVD_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
- Dr. Noon CVD_FDA : Under the DeNovo Pathway
AI-powered Solution for
Cardiovascular Disease Risk Assessment
Dr. Noon CVD is a retina-based AI solution that predicts the future risk of cardiovascular disease.
It is the world’s first AI-powered solution utilizing retinal imaging to assess cardiovascular risk.
In Korea, it is commercialized with regulatory approval and reimbursement, making it widely accessible in clinical practice. Globally, it is commercialized in Dubai, UK, and Italy with regulatory approvals in 8 regions. Adopted in many hospitals and clinics, it provides accurate, non-invasive, and cost-effective methods.
How Dr. Noon CVD Uses AI
for Cardiovascular Health
Cardiovascular diseases develop silently over time and can lead to fatal conditions such as heart
attacks and strokes. Early detection is crucial to prevent severe complications before they occur.
Watch the video to learn how Dr. Noon CVD leverages AI and retinal imaging
for cardiovascular risk assessment.
Patient Journey
Follow the step-by-step journey of a patient visiting the hospital
and see how they meet Dr. Noon CVD.
-
STEP 1
If a patient is suspected of being at risk for cardiovascular disease—such as those with diabetes, hypertension, hyperlipidemia, or obesity
—the Dr. Noon CVD is recommended. -
STEP 2
A medical professional conducts a consultation, reviewing the patient’s medical history and current health status.
-
STEP 3
Take one picture of each eye to capture the retina.
-
STEP 4
The retinal images are uploaded to the healthcare institution’s server or cloud platform.
-
STEP 5
Artificial intelligence automatically
analyzes the images to assess the patient’s risk of cardiovascular disease. -
STEP 6
The risk of cardiovascular disease is determined through an automatically generated report.
Comparison with
Traditional Methods
A comparison with traditional methods highlights Dr. Noon CVD's superior performance
over conventional tests in assessing cardiovascular risk.
- Heart CT
- Carotid Ultrasound
- Pulse Wave Velocity
- Type
- Ease of Use
- Accessibility
- Footprint & Equipment
- Radiation Exposure
- Cost
- Turnaround Time
-
Operator
Dependency -
Risk
Prediction Accuracy
-
Type
Retina-based coronary
calcium score prediction -
Ease of Use
Easy (Simple retinal photo, <3 min)
-
Accessibility
High (Usable in primary care)
-
Footprint & Equipment
Small (Requires only fundus camera)
-
Radiation Exposure
None
-
Cost
Low (Affordable)
-
Turnaround Time
Fast (Instant AI result, <3 min)
-
Operator
DependencyLow (Minimal training needed) -
Risk
Prediction AccuracyHigh (Comparable to CACS)
Key Benefits
-
Predicts cardiovascular disease risk with accuracy comparable to
heart CT. -
Requires no needle injection or radiation exposure, making it comfortable for patients.
-
Compared to conventional cardiovascular screenings, the test is available at a more affordable cost.
-
Results are delivered promptly, allowing patients to receive their reports without delay.
-
Designed for wide use across clinics, hospitals, and health screening centers, enabling broader access to risk assessment.
Our Customers
Dr. Noon CVD is utilized by leading hospitals, clinics, and check-up centers in Korea and worldwide,
offering early cardiovascular risk assessments to patients.






















Publications & Webinars
Check out the clinical validations and related research papers that demonstrate
the accuracy, safety, and real-world effectiveness of Dr. Noon CVD.
-
Original Paper
Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores
-
Original Paper
Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI
-
Original Paper
Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank