Eyelomics turns visual fields, OCT, fundus imaging, and clinical data into a single, reproducible intelligence layer — severity, progression, and responder signals from the same AI engine.
Every glaucoma visit generates visual fields, OCT, fundus images, IOP, and clinical history. Yet most platforms treat each in isolation — returning raw numbers with no unified severity signal, no trajectory, and no decision support.
Staging varies by clinician, by device, by practice. Clinical trials over-recruit across the severity spectrum, inflating sample size and diluting efficacy signals. The data is there. The intelligence layer isn't.
Modality-agnostic severity staging from any high-dimensional ophthalmic dataset. A 30-step granular cluster model maps to four clinical stages — Normal, Early, Moderate, Advanced — validated against gold-standard references.
Patients classified into four progression phenotypes — No Progression, Stable, Slow, Rapid — using multi-modal data. Anchored to cluster transitions, not noisy point-to-point measurements, enabling earlier detection of meaningful change.
AI-derived patient profiles identify who is likely to respond to a given intervention — enabling trial enrichment, smaller sample sizes, and precision treatment selection across clinical and pharma applications.
Every modality. One severity profile per patient.
30 minutes to explore synergies between Eyelomics and your network or program.
Severity intelligence output demonstrated on de-identified multi-modal data.
Co-development, licensing, or real-world evidence collaboration — we're flexible.