Eyedentity Medical
Eyedentity Medical provides an AI-powered decision support platform that helps opticians and ophthalmologists identify suspected uveal melanoma from retinal images. The system is built on longitudinal clinical data and validated in peer-reviewed studies, with the goal of reducing unnecessary referrals while maintaining high sensitivity for malignant cases. The platform is currently being evaluated in real-world pilot programs and is progressing toward CE-MDR submission.
- Artificial Intelligence
- Digital Health
- Healthcare Technology
- 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
Founders
Product
Problem
Uveal melanoma is a rare but serious intraocular malignancy that is often detected late due to its subtle presentation on routine retinal imaging. Many suspected cases are referred to specialized centers, leading to unnecessary patient anxiety and healthcare costs when lesions turn out to be benign, while delayed detection of true malignancies can be sight- and life-threatening.
Solution
Eyedentity Medical provides an AI-powered clinical decision support platform that analyzes retinal images to identify suspected uveal melanoma with high sensitivity. The system is built on unique longitudinal clinical data and validated in peer-reviewed studies, enabling it to support early detection in routine optometry and ophthalmology practice. The platform is designed to integrate seamlessly into existing imaging workflows, providing a risk assessment that helps clinicians decide whether a referral to a specialist is warranted. By reducing unnecessary referrals while maintaining high sensitivity for malignant cases, Eyedentity helps streamline care pathways and improve patient outcomes.
Target Audience
Primary users are opticians and ophthalmologists who perform retinal imaging as part of routine eye examinations and need decision support to identify suspected uveal melanoma and guide appropriate referrals.
Features
- Machine learning model trained on unique longitudinal clinical data for uveal melanoma detection
- Risk assessment output based on retinal image analysis, designed to support clinical judgment
- Integration with existing imaging workflows in optometry and ophthalmology practices
- Validated in peer-reviewed studies with ongoing real-world pilot programs
- Platform architecture designed for future expansion into additional ophthalmic applications