L1 ⚛ L1-513 ⊙ Testnet

Diabetic Retinopathy Grading from Fundus Imaging (PWDR)

Medical Imaging · Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout

⬇ Download bundle 🚀 Submit to mainnet — earn 5% royalty

🎁 Contribute & earn PWM

Improve L1-513 — submit with or without uploading. Accepted submissions earn the 1/16 modify share of A(t).

✨ Submit without uploading free check · 1×

One click — no files, no typing. We submit a check of L1-513 for you; it goes through review and earns the 1/16 share once. One free no-upload Principle per account.

You'll be asked to log in first — then it's one click, no files.

⬆ Or submit by uploading your files

Upload your .md + .json as a modification of L1-513. Unlimited.

🛠 Helper: build your files in ChatGPT / Gemini / Claude

Layer template

⬇ principle.md ⬇ principle.json

Fork L1-513

⬇ L1-513.md ⬇ L1-513.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-513 (Diabetic Retinopathy Grading from Fundus Imaging (PWDR)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-513.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-513.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫_A dA `L.etdrs_threshold_classifier` `L.lesion_count_aggregation` `L.lesion_segmentation` `L.vessel_segmentation` `L.fundus_acquisition` x + n,    n ~ 𝒩(0, σ²)

world state x (1D etdrs severity grade)

→

L · fundus acquisition `L.fundus_acquisition`

→

L · vessel segmentation `L.vessel_segmentation`

→

L · lesion segmentation `L.lesion_segmentation`

→

L · lesion count aggregation `L.lesion_count_aggregation`

→

L · etdrs threshold classifier `L.etdrs_threshold_classifier`

→

Spatial integration ∫_A dA

→

observation y (photon detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-513.md

# ⚛ L1 Principle — Diabetic Retinopathy Grading from Fundus Imaging (PWDR) **ID:** `L1-513` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D etdrs severity grade > **📡 Carrier:** photon · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41553387 --- ## 🧠 1. Introduction **Diabetic Retinopathy Grading from Fundus Imaging (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D etdrs severity grade** space, within the **Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout** sub-domain of **Medical Imaging**. Measurements consist of photons collected by an optical detector via a **fundus color photography with etdrs grading** sensing mechanism. The forward operator applies, in order: L · fundus acquisition operator; L · vessel segmentation operator; L · lesion segmentation operator; L · lesion count aggregation operator; L · etdrs threshold classifier operator; pixel-level spatial averaging on the detector. Observations are corrupted by additive Gaussian noise. 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). ## ⚙ 2. Forward Model Physical chain: **x** → L · fundus acquisition → L · vessel segmentation → L · lesion segmentation → L · lesion count aggregation → L · etdrs threshold classifier → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.etdrs_threshold_classifier` `L.lesion_count_aggregation` `L.lesion_segmentation` `L.vessel_segmentation` `L.fundus_acquisition` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.fundus_acquisition` | L · fundus acquisition operator | | `L.vessel_segmentation` | L · vessel segmentation operator | | `L.lesion_segmentation` | L · lesion segmentation operator | | `L.lesion_count_aggregation` | L · lesion count aggregation operator | | `L.etdrs_threshold_classifier` | L · etdrs threshold classifier operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout | | Carrier | photon | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_etdrs_severity_grade | | Noise model | gaussian | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 5 | ## 📡 4. Measurement Model 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). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | weighted_kappa_etdrs | ## 📏 5. Operating Range (Ω) **Center problem class:** `diabetic_retinopathy_etdrs_pwdr` · **Forward operator:** `fundus_etdrs_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 2048 | | W | px | 2048 | | Snr db | dB | 30 | | Image quality | — | 0 | | Media opacity | — | 0 | | Pixel resolution um | µm | 5 | | Pupil dilation state | — | 1 | | Field of view degrees | — | 45 | | Peripheral field truncation | — | 0 | | Grader inter rater variability calibration | — | 0 | | Manual vs automated lesion segmentation disagreement | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 512 – 8192 | | W | px | 512 – 8192 | | Image quality | — | 0.0 – 0.5 | | Media opacity | — | 0.0 – 0.4 | | Field of view degrees | — | 30 – 200 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.85_accuracy | Metric | Range | |---|---| | Categorical accuracy | 0.5 – 0.99 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **categorical_accuracy**, with κ = `100` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x2cdbd05721a0533f7f9f3b0b29ab053ec0bc9e17cf9b7914ea0fc2e2975cdd35` - **Chain tx hash:** `0xd162e26a36f9adcd19383925254a415bde3896422ea4fb0a5d9474cb1b8f1458` - **Chain block:** `41553387` --- ## File Mapping This bundle consists of: `L1-513.md`, `L1-513.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-513.md` | Source of truth — edit this | Human or LLM | | `L1-513.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-513`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

💬

Ask the PWM AI agent AI

Ask about this Diabetic Retinopathy Grading from Fundus Imaging (PWDR) — or how to improve it & earn PWM

Free to search; log in to chat with the agent & earn PWM. How it works ↗

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Digital Twin 1 child ▾

L2-513-001 Diabetic Retinopathy Grading from Fundus Imaging (PWDR) — Spec Skeleton

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-513",
  "chain_block": 41553387,
  "chain_hash": "0x2cdbd05721a0533f7f9f3b0b29ab053ec0bc9e17cf9b7914ea0fc2e2975cdd35",
  "chain_tx_hash": "0xd162e26a36f9adcd19383925254a415bde3896422ea4fb0a5d9474cb1b8f1458",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 100,
    "metric": "categorical_accuracy",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "categorical_accuracy",
    "regime": "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\u0027s 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).",
    "secondary": "weighted_kappa_etdrs"
  },
  "physics_fingerprint": {
    "L_DAG": 5.0,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "spatial",
    "noise_model": "gaussian",
    "primitives": [
      "L.fundus_acquisition",
      "L.vessel_segmentation",
      "L.lesion_segmentation",
      "L.lesion_count_aggregation",
      "L.etdrs_threshold_classifier",
      "int.spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "fundus_color_photography_with_etdrs_grading",
    "solution_space": "1D_etdrs_severity_grade",
    "sub_domain": "Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout",
    "title": "Diabetic Retinopathy Grading from Fundus Imaging (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "fundus_etdrs_pwdr_forward",
      "fundus_icdr_pwdr_forward",
      "fundus_etdrs_per_quadrant_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "field_of_view_degrees",
      "pixel_resolution_um",
      "SNR_dB",
      "image_quality",
      "pupil_dilation_state",
      "media_opacity",
      "peripheral_field_truncation",
      "manual_vs_automated_lesion_segmentation_disagreement",
      "grader_inter_rater_variability_calibration"
    ],
    "allowed_problem_classes": [
      "diabetic_retinopathy_etdrs_pwdr",
      "diabetic_retinopathy_referrable_pwdr",
      "diabetic_retinopathy_per_quadrant_pwdr"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.85_accuracy",
      "forward_operator": "fundus_etdrs_pwdr_forward",
      "input_format": "color_fundus_image_with_grade_label",
      "omega": {
        "H": 2048,
        "SNR_dB": 30,
        "W": 2048,
        "field_of_view_degrees": 45,
        "grader_inter_rater_variability_calibration": 0.0,
        "image_quality": 0.0,
        "manual_vs_automated_lesion_segmentation_disagreement": 0.0,
        "media_opacity": 0.0,
        "peripheral_field_truncation": 0.0,
        "pixel_resolution_um": 5.0,
        "pupil_dilation_state": 1.0
      },
      "problem_class": "diabetic_retinopathy_etdrs_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "H": [
        512,
        8192
      ],
      "W": [
        512,
        8192
      ],
      "field_of_view_degrees": [
        30,
        200
      ],
      "image_quality": [
        0.0,
        0.5
      ],
      "media_opacity": [
        0.0,
        0.4
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Retinal vasculature reconstruction with diabetic retinopathy ETDRS-grade categorical readout",
  "title": "Diabetic Retinopathy Grading from Fundus Imaging (PWDR)"
}

🎁

Earn PWM by promoting this to mainnet

• PWM emission on accept — your share of A(t) (1/16 modify · 3/16 new)
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Go to submit ↑

How: use the “Contribute & earn PWM” panel at the top — either Submit without uploading (one click) or upload your files. Then the review runs and, on accept, it’s promoted to mainnet.