AI Retinal Image Analysisfor Fundus Photography

Fundus image registration, retinal segmentation, and interpretable metrics for clinical and research workflows — backed by world-class research at the University of Edinburgh.

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Eye disease is a global,
and growing, problem.

589M
adults living with diabetes worldwide
projected to rise to 853M by 2050
Source: IDF Diabetes Atlas 2025
196M
people estimated to be living with AMD
projected to reach 288M by 2040
Source: Wong et al. via NCBI Bookshelf
80M
people worldwide living with glaucoma
about half are unaware they have it
Source: Weinreb, Glaucoma Research Foundation

Retinal disease is rising while screening and follow-up still rely on slow manual review, so EES helps teams scale retinal analysis with:

reproducible image registration for accurate longitudinal comparison
automated anatomical segmentation across key retinal structures
interpretable metrics that make progression easier to quantify and triage earlier

This gives optometrists, clinicians, and researchers a more scalable workflow without sacrificing rigor.

From Image to Insight

Step 1
Capture
Retinal fundus images are acquired with standard imaging equipment already used in clinical practice.
Step 2
Analyse
Our deep-learning models segment vessels, optic disc, fovea, and other key anatomical structures.
Step 3
Insight
Clinicians access a dashboard with visual overlays, retinal metrics, and structured reports.

Compatible with standard fundus image formats — DICOM, TIFF, PNG, BMP.

Purpose-Built Solutions for Retinal Imaging

Our product suite brings AI-powered analysis into clinical workflows.

Flagship

FundusLab

Image registration and analysis for fundus photography

FundusLab automates the analysis of fundus images, leveraging deep learning to segment key retinal structures and register longitudinal studies. Designed for optometrists, by retinal researchers — it delivers rapid, interpretable insights to support clinical decision-making and research.

Request FundusLab Demo

Coming soon

Next product

OCT-grade analytics, in development

We are extending the EES platform to additional imaging modalities. Reach out if you would like to be notified when it ships or to participate in early validation studies.

Get notified

All EES products are built on the same research-validated AI foundation, with continuous updates as clinical evidence evolves.

Engineered for clinical and research rigor

Every output is designed to be transparent, reproducible, and ready to integrate into existing workflows.

Deep Learning Segmentation

Trained on clinically validated datasets for high sensitivity and specificity across vessels, disc, and fovea.

Rapid Processing

Results are delivered in seconds, integrating seamlessly into existing clinical workflows and research pipelines.

Interpretable Outputs

Explainable AI visualisations — overlays, heatmaps, and metrics — that domain experts can trust and act on.

Research-Grade Accuracy

Developed and validated by University of Edinburgh researchers, grounded in peer-reviewed methodology.

Built by Researchers, for Optometrists

Our team combines deep expertise in retinal imaging, computer vision, and clinical research — with direct experience working alongside ophthalmologists.