AI service — AI Medical · Ophthalmology
Retinal image analysis
Grade retinal disease from fundus photographs and OCT, quantify vessel structure, and run label-efficient screening from your own archive.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Retinal imaging is high volume and highly standardised, which is why foundation models work well here: one pass over an image produces an embedding that a classifier of your own trains on with a few hundred labels rather than tens of thousands.
Images arrive from your camera or OCT export and come back as grades, embeddings, vessel maps or segmentations. Oculomics work — vessel calibre, tortuosity and fractal measures associated with systemic disease — runs from the same pipeline.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the retinal image analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Screening programmes — Grade large volumes of fundus photographs and route the graded cases.
- Label-efficient classifiers — Train a disease endpoint on embeddings from your own annotated set.
- Oculomics research — Quantify vessel structure across a cohort for cardiovascular association work.
More Ophthalmology services
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