Mediwhale

retinal imaging, AI, cardiovascular risk assessment

diagnostics

mediwhale.com·Seoul, South Korea·Series C Plus

01Overview 02People 03Projects 04Concepts 05Patents 06Public records 07Belief evolution

Plate C · C-5PRZ

Summary · Eternal Search editorial

Mediwhale retinal imaging, AI, cardiovascular risk assessment

Mediwhale leverages retinal imaging and AI to support early detection, personalized risk assessment, and preventive care for cardiometabolic disease. Its Dr.Noon CVD product is described as a retina-based AI risk prediction solution for future cardiovascular disease risk. The company says its AI products have regulatory approval and reimbursement in Korea and are available in hospitals in Dubai, the UK, and Italy.

The published score reflects a curated read of public evidence across 16 dimensions. Each dimension below shows the rationale we used. Disagree with a reading? Tell us where we are wrong →

Plate C·01 · Scientific profile

All 16 dimensions
on the record.

Composite38

Chain scale100 pts

Dimensions16

Plate C·02 · Where it sits among peers

Score vs. proximity
to longevity goals.

Higher up = higher Eternal Search score. Further right = closer to achieving real longevity outcomes (early mechanism → translational → near-term readout). Bubble size = capital deployed. Watch for MISMATCHES: big bubbles on the left are well-capitalized but far from goals; small bubbles on the right are doing more with less.

Reading

Mediwhale — Near-term readout · Lower middle. Derived deterministically from 4 extracted project(s). Most-advanced status: approved. Band: near_readout. Aging-relevance not LLM-reviewed in this pass.

Source: Deterministic

At a glance

Webmediwhale.com

HQSeoul, South Korea

StageSeries C Plus

Publications

22 on file

012026Association between Open-Angle Glaucoma Progression and Cardiovascular Risk Predicted by a Deep Learning-Based Fundus Imaging Model.Ophthalmology. Glaucoma→022026Retinal biological age correlates with bone mineral density and fracture risk score and predicts incident osteoporosis.PLOS digital health→032026Artificial intelligence based retinal imaging for cardiovascular risk and statin guidance in retinal vein occlusion.American journal of preventive cardiology→042026Does lens opacity matter? The effect of cataract on deep learning based cardiovascular disease risk scores from fundus photos.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)→052026Clinical utility of an AI-based retinal imaging model for cardiovascular risk prediction in hypertensive retinopathy.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie→062024Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study.The lancet. Healthy longevity→072024Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.Eye and vision (London, England)→082024Deep learning model for intravascular ultrasound image segmentation with temporal consistency.The international journal of cardiovascular imaging→092024A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.JAMA ophthalmology→102024Correction: Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.Eye and vision (London, England)→112023Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI.Journal of the American Medical Informatics Association : JAMIA→122023Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors.NPJ digital medicine→132023Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.European heart journal. Digital health→142023Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.BMC medicine→152023Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI—→162023Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores—→172021Automatic segmentation of corneal deposits from corneal stromal dystrophy images via deep learning.Computers in biology and medicine→182021Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens.Translational vision science & technology→192020Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level.Translational vision science & technology→202019Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.NPJ digital medicine→212018Deep Learning Is Effective for Classifying Non-referable versus Referable Eye Condition using Fundus Photographs—→222017Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database—→

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0-100 chain-logic scale · 15 dimensions · scored on public evidence

Mediwhale

All 16 dimensionson the record.

Score vs. proximityto longevity goals.

Publications

Loading

All 16 dimensionson the record.

Score vs. proximityto longevity goals.

Publications

All 16 dimensions
on the record.

Score vs. proximity
to longevity goals.

All 16 dimensions
on the record.

Score vs. proximity
to longevity goals.