Diabetic Retinopathy Grading from Fundus Imaging (PWDR) L1-513

Medical ImagingRetinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readoutδ=3 · standardL_DAG = 5📋 Stub — not mineable

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Unclaimed Principle — open for contribution

This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.

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Forward model E

S t a g e 1 (L 1 - 049) : I_{f} u n d u s (x, y, R GB) \to v esse l_{s} k e l e t o n, mi cr o an e u r y s m_{m} a p, h e m or r ha g e_{m} a s k, e xu d a t e_{m} a s k, n eo v a sc u l a r i z a t i o n_{m} a s k, o pt i c_{d} i s c_{l} oc, ma c u l a_{l} oc . S t a g e 2 (E T D R S r u l e) : l ab e l = E T D R S_{t} h r es h o l d (l es i o n_{c} o u n t s, t o p o g r a p hi c_{l} oc a t i o n s) p er W i l k in so n e t a l .2003 I C D R cr i t er ia .

Diabetic Retinopathy Grading from Fundus Imaging (PWDR): wraps L1-049 fundus analytical core with the international ETDRS / ICDR clinical-grading rule. Stage 1 (analytical, from L1-049): recover retinal vasculature reconstruction from color fundus photograph including vessel skeleton, lumen diameters, optical-disc and macular landmarks, plus segmented lesion masks (microaneurysms, hemorrhages, hard exudates, cotton-wool spots, IRMA, venous beading, neovascularization). Stage 2 (deterministic threshold): apply ETDRS-grade thresholds to lesion counts and topographic locations to yield categorical severity {none, mild_NPDR, moderate_NPDR, severe_NPDR, PDR}. The threshold function is piecewise-constant on lesion-count features and locally Lipschitz-continuous on the underlying vasculature reconstruction. Difficulty tier delta = 3 inherited from L1-049 plus a small ETDRS-rule complexity. Mismatch parameters: image_quality, pupil_dilation_state, media_opacity, peripheral_field_truncation, manual_vs_automated_lesion_segmentation_disagreement, grader_inter_rater_variability_calibration.

L-DAG

L.fundus_acquisition -> L.vessel_segmentation -> L.lesion_segmentation -> L.lesion_count_aggregation -> L.etdrs_threshold_classifier -> int.spatial

L.fundus_acquisitionL.vessel_segmentationL.lesion_segmentationL.lesion_count_aggregationL.etdrs_threshold_classifierint.spatial

Well-posedness W

Existence:: true
Uniqueness:: conditional
Stability:: conditional
κ:: 100

Existence inherited from L1-049 fundus core. Uniqueness conditional on adequate image quality (no severe media opacity; pupil dilation enables peripheral retinal coverage); ETDRS-grade transition boundaries (the 4-2-1 rule) form measure-zero hypersurfaces in lesion-count space. Stability inherits L1-049's kappa_eff plus a small additive contribution from grader_inter_rater_variability_calibration. The threshold-continuity proof in discrete_readout demonstrates that ETDRS-grade is a well-defined function of lesion counts; misclassification probability scales linearly with lesion-count error away from the 4-2-1 hypersurface boundary. Joint Hadamard well-posedness for the coupled vasculature-reconstruction + ETDRS-threshold forward established by Wilkinson 2003 (foundational ICDR paper), Abramoff 2018 (FDA-cleared autonomous AI), Gulshan 2016 (deep-learning grading benchmarks), Ting 2017 (Asian-population validation), Bhaskaranand 2019 (real-world performance), and Solomon 2017 (DR screening guidelines).

Solvability C

Solver class:: linear-operator + convex optimisation [vessel + lesion segmentation via L1-049] + categorical-readout [ETDRS rule] | end-to-end deep neural [DeepDR, IDx-DR, EyeArt] with explicit physics-informed lesion-segmentation regularization
Convergence rate q:: 2
Complexity:: O(H * W * (N_vessel_classes + N_lesion_classes)) for stage 1; O(N_lesions) for stage 2 ETDRS-threshold; total stage-1-dominated

Specs (0)

No L2 specs registered yet for this principle.