General Research Information
EyeMap.ai Labs develops and validates AI systems for population-scale retinal disease screening, with a focus on clinical deployability and real-world impact.
Mission & Approach
The retinal fundus photograph is one of medicine's most information-dense, least exploited investigations. In under five minutes, a non-invasive snapshot of the retina can reveal not only ocular disease but systemic conditions — including hypertension, diabetes, and early cardiovascular risk — years before clinical symptoms emerge.
EyeMap.ai Labs was founded to close the gap between what retinal imaging can show and what current clinical workflows actually capture. We train, validate, and deploy deep learning models that operate on standard fundus images, making population-level screening feasible at point-of-care settings including optometry practices.
Our work is guided by three commitments: clinical validity (models are benchmarked against expert ophthalmologist grading), reproducibility (we document training data, architectures, and evaluation protocols), and equity (we test model performance across demographic subgroups to detect and mitigate bias).
OCULOMICS is the study of the eye as an indicator of overall health, coined in 2020 by Prof. Denniston. Our programme extends this framework by using retinal imaging biomarkers to investigate cardiometabolic and neurovascular risk patterns that may be detectable before overt clinical disease.
All models are developed with the EU AI Act and MDR regulatory pathway in mind. We do not present AI outputs as diagnostic conclusions — they are screening flags that direct clinician attention. Human oversight is central to every deployment design.
AI Models
Binary and 2-class severity grading. Detects drusen, pigmentary abnormalities, geographic atrophy, and neovascularisation.
5-level ETDRS-aligned severity classification from No DR through Proliferative DR. Integrates with primary care diabetes management pathways.
Automated cup-to-disc ratio measurement with clinical significance thresholds. Flags cases for glaucoma specialist review.
Clinical Partnerships
Partnership discussions are underway with multiple clinics to support prospective clinical validation of retinal AI screening workflows and dataset expansion.
Working with Dr. Florian Balta to validate retinal pathology detection models in a specialized ophthalmology setting.
Prospective collaboration to evaluate stroke-related screening workflows in emergency-care contexts and diverse patient populations.
Partnership discussions ongoing to expand clinical validation coverage and access to additional patient cohorts.
Partnership discussions ongoing to expand clinical validation coverage and access to additional patient cohorts.
Partnership discussions ongoing to expand clinical validation coverage and access to additional patient cohorts.
Partnership discussions ongoing to expand clinical validation coverage and access to additional patient cohorts.