Models in this group take a colour fundus photograph or an OCT scan; 3D models take a full OCT volume. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
RETFound / RETFound-DINOv2 / RETFound-DINOv3
Vendor: Moorfields Eye Hospital / UCL
What it does: is the reference retinal foundation model: fine-tuned it reaches AUROC 0.82–0.94 on sight-threatening disease, and it also predicts incident heart failure and myocardial infarction above baseline — the clearest evidence for oculomics in an open model.
AUROC 0.822–0.943 for sight-threatening eye disease; diabetic retinopathy AUROC 0.943 on APTOS-2019; also predicts incident heart failure and MI above baselines (independent, Nature 2023).
RetFiner-RETFound
Vendor: RetFiner authors
What it does: refines the RETFound representation, improving downstream accuracy without retraining from scratch.
Task-dependent; reported improvements are benchmark-specific.
RetFiner-UrFound
Vendor: RetFiner authors
What it does: applies the same refinement to the UrFound base for retinal downstream tasks.
Task-dependent; reported improvements are benchmark-specific.
RetFiner-VisionFM
Vendor: RetFiner authors
What it does: applies the same refinement to VisionFM, for teams already standardised on it.
Task-dependent; reported improvements are benchmark-specific.
VisionFM
Vendor: Zhejiang University / Shanghai AI Lab, CUHK collaborators
What it does: covers eight ophthalmic modalities — fundus, OCT, slit-lamp, ultrasound, angiography and more — and matched or exceeded junior ophthalmologists on several diagnostic tasks.
Matches or exceeds junior ophthalmologists on several diagnostic tasks across 8 modalities (independent, NEJM AI 2024).
OCTCube / OCTCube-M
Vendor: University of Washington and community
What it does: reads the OCT volume rather than single B-scans, which is worth 0.02–0.06 AUROC over 2D baselines because pathology spreads through depth.
Outperforms 2D baselines on 3D OCT disease detection, AUROC +0.02–0.06 (independent).
AutoMorph
Vendor: Moorfields / UCL
What it does: measures the vessels rather than labelling the disease — calibre, tortuosity, fractal dimension and disc metrics — which is what oculomics research runs on.
Vessel segmentation Dice about 0.80–0.83; artery/vein AUC about 0.93 (independent, TVST 2022).
Diabetic retinopathy grading CNNs
Vendor: Community, EyePACS-trained (EfficientNet / Inception)
What it does: grades referable diabetic retinopathy at AUROC 0.94–0.98 on the public sets. Camera and population shift is where it loses accuracy, and where we benchmark first.
Referable DR AUROC 0.94–0.98 on EyePACS and Messidor; degrades with camera and population shift (independent).
OCT2017 classifiers
Vendor: Community (Kermany dataset lineage)
What it does: classifies four common retinal pathologies on a B-scan. The headline 96–98 percent is on its original test set only; expect a substantial drop elsewhere, so treat it as a starting baseline.
About 96–98 percent on the original test set; heavily over-fitted to that dataset, expect a large drop elsewhere (independent).