Eyedentity
Eyedentity Medical provides an AI-powered clinical decision support platform that analyzes existing fundus images during routine eye exams, delivering a risk assessment for suspected uveal melanoma within seconds. The system runs automatically in the background, checks image quality, and returns a score and recommendation while the patient is still in the exam chair—using the same camera and visit, with no images stored. The model is trained on longitudinal clinical data in partnership with St. Erik Eye Hospital and has been validated in peer-reviewed studies.
- Healthcare Technology
- Digital Health
- Artificial Intelligence
- Data & Analytics
- Software Only
Funding
Raised to date
€1.3MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Est. $1.4Macross 1 round
€1.3M · Seed
Announced Aug 2026 · from article date
Disclosed cumulative: $1.4M
Founders
Founder details are not available yet.
Product
Problem
Over one million retinal images are taken every day, yet most receive only a single look by one person in one visit. Getting a second opinion involves days of waiting and a fee per image, which can delay detection of serious conditions like uveal melanoma, where late diagnosis is sight- and life-threatening.
Solution
Eyedentity Medical provides an AI-powered clinical decision support platform that analyzes existing fundus images during routine eye exams and delivers decision support within seconds. The system checks image quality and flags poor photos for retake, then runs machine learning analysis in the background the moment the photo is taken—with no extra clicks and no images stored. The result is a risk assessment and recommendation delivered in the exam room while the patient is still in the chair, using the same camera and same visit. The platform is trained on longitudinal clinical data in partnership with St. Erik Eye Hospital and is designed to integrate into existing optometry and ophthalmology workflows, reducing unnecessary referrals while maintaining high sensitivity for suspected uveal melanoma.
Target Audience
Primary users are opticians, optometrists, and ophthalmologists who perform routine retinal imaging during eye exams and need decision support to identify suspected uveal melanoma and guide appropriate specialist referrals.
Features
- Machine learning model that analyzes fundus images automatically in the background with no user interaction required
- Instant image quality assessment that flags poor-quality photos for retake before analysis
- Risk assessment output for suspected uveal melanoma, delivered in seconds while the patient is still in the exam chair
- Built on unique longitudinal clinical data in partnership with St. Erik Eye Hospital, with clinical leadership by a professor ranked fourth globally in uveal melanoma research
- Validated in peer-reviewed studies, including testing with 802 fundus photographs and in a study with 29 opticians and optometrists
- No image storage, preserving patient privacy within the existing imaging workflow
- Platform architecture designed for future expansion into additional ophthalmic applications