L1 ⚛ L1-530 ⊙ Testnet

Age-Related Macular Degeneration OCT Grading (PWDR)

Medical Imaging · Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout

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⬇ principle.md ⬇ principle.json

Fork L1-530

⬇ L1-530.md ⬇ L1-530.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-530 (Age-Related Macular Degeneration OCT Grading (PWDR)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-530.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-530.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.amd_stage_classifier` `L.ga_segmentation` `L.fluid_detection` `L.drusen_detection` `L.layer_segmentation` `L.oct_acquisition` x + n,    n ~ 𝒩(0, σ²)

world state x (1D amd stage)

→

L · oct acquisition `L.oct_acquisition`

→

L · layer segmentation `L.layer_segmentation`

→

L · drusen detection `L.drusen_detection`

→

L · fluid detection `L.fluid_detection`

→

L · ga segmentation `L.ga_segmentation`

→

L · amd stage classifier `L.amd_stage_classifier`

→

Spatial integration ∫_A dA

→

observation y (photon detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-530.md

# ⚛ L1 Principle — Age-Related Macular Degeneration OCT Grading (PWDR) **ID:** `L1-530` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D amd stage > **📡 Carrier:** photon · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41553374 --- ## 🧠 1. Introduction **Age-Related Macular Degeneration OCT Grading (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D amd stage** space, within the **Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout** sub-domain of **Medical Imaging**. Measurements consist of photons collected by an optical detector via a **oct with amd grading** sensing mechanism. The forward operator applies, in order: L · oct acquisition operator; L · layer segmentation operator; L · drusen detection operator; L · fluid detection operator; L · ga segmentation operator; L · amd stage classifier operator; pixel-level spatial averaging on the detector. Observations are corrupted by additive Gaussian noise. Existence inherited from L1-042. Uniqueness conditional on adequate OCT image quality (signal strength index > 6/10 typical). Stability dominated by patient_motion + shadowing_artifact. Joint Hadamard well-posedness established by AREDS Research Group 2001 (foundational), Beckman 2013 (consensus classification), Schmidt-Erfurth 2018 (OCT in AMD review), De Fauw 2018 (DeepMind referrable disease classification benchmark). ## ⚙ 2. Forward Model Physical chain: **x** → L · oct acquisition → L · layer segmentation → L · drusen detection → L · fluid detection → L · ga segmentation → L · amd stage classifier → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.amd_stage_classifier` `L.ga_segmentation` `L.fluid_detection` `L.drusen_detection` `L.layer_segmentation` `L.oct_acquisition` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.oct_acquisition` | L · oct acquisition operator | | `L.layer_segmentation` | L · layer segmentation operator | | `L.drusen_detection` | L · drusen detection operator | | `L.fluid_detection` | L · fluid detection operator | | `L.ga_segmentation` | L · ga segmentation operator | | `L.amd_stage_classifier` | L · amd stage classifier operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout | | Carrier | photon | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_amd_stage | | Noise model | gaussian | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 5.6 | ## 📡 4. Measurement Model Existence inherited from L1-042. Uniqueness conditional on adequate OCT image quality (signal strength index > 6/10 typical). Stability dominated by patient_motion + shadowing_artifact. Joint Hadamard well-posedness established by AREDS Research Group 2001 (foundational), Beckman 2013 (consensus classification), Schmidt-Erfurth 2018 (OCT in AMD review), De Fauw 2018 (DeepMind referrable disease classification benchmark). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | sensitivity_for_referable_AMD | ## 📏 5. Operating Range (Ω) **Center problem class:** `amd_oct_pwdr` · **Forward operator:** `amd_oct_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 512 | | W | px | 512 | | Z | — | 128 | | Snr db | dB | 30 | | Media opacity | — | 0 | | Wavelength nm | nm | 850 | | Patient motion | — | 0 | | Macular centration | — | 0 | | Shadowing artifact | — | 0 | | Axial resolution um | µm | 5 | | Lateral resolution um | µm | 15 | | Vitreous floater artifact | — | 0 | | Manual grader inter rater kappa | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 128 – 1024 | | W | px | 128 – 1024 | | Z | — | 32 – 512 | ## 🎯 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 = `80` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xd82995adf798cc5feb705e3a2ffecbe73c89608810271eb2ec27fef476e0d9aa` - **Chain tx hash:** `0xa068758abb70617ff5e1056181bc24c7f3cb4ea230f73b5ce303903bcf6e3b00` - **Chain block:** `41553374` --- ## File Mapping This bundle consists of: `L1-530.md`, `L1-530.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-530.md` | Source of truth — edit this | Human or LLM | | `L1-530.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 `L1-530`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-530-001 Age-Related Macular Degeneration OCT Grading (PWDR) — Spec Skeleton

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📋 JSON metadata

{
  "artifact_id": "L1-530",
  "chain_block": 41553374,
  "chain_hash": "0xd82995adf798cc5feb705e3a2ffecbe73c89608810271eb2ec27fef476e0d9aa",
  "chain_tx_hash": "0xa068758abb70617ff5e1056181bc24c7f3cb4ea230f73b5ce303903bcf6e3b00",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 80,
    "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 L1-042. Uniqueness conditional on adequate OCT image quality (signal strength index \u003e 6/10 typical). Stability dominated by patient_motion + shadowing_artifact. Joint Hadamard well-posedness established by AREDS Research Group 2001 (foundational), Beckman 2013 (consensus classification), Schmidt-Erfurth 2018 (OCT in AMD review), De Fauw 2018 (DeepMind referrable disease classification benchmark).",
    "secondary": "sensitivity_for_referable_AMD"
  },
  "physics_fingerprint": {
    "L_DAG": 5.6,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "spatial",
    "noise_model": "gaussian",
    "primitives": [
      "L.oct_acquisition",
      "L.layer_segmentation",
      "L.drusen_detection",
      "L.fluid_detection",
      "L.ga_segmentation",
      "L.amd_stage_classifier",
      "int.spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "oct_with_amd_grading",
    "solution_space": "1D_amd_stage",
    "sub_domain": "Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout",
    "title": "Age-Related Macular Degeneration OCT Grading (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "amd_oct_pwdr_forward",
      "amd_areds_pwdr_forward",
      "amd_beckman_pwdr_forward",
      "amd_with_octa_overlay_pwdr_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "Z",
      "wavelength_nm",
      "axial_resolution_um",
      "lateral_resolution_um",
      "macular_centration",
      "SNR_dB",
      "media_opacity",
      "patient_motion",
      "shadowing_artifact",
      "vitreous_floater_artifact",
      "manual_grader_inter_rater_kappa"
    ],
    "allowed_problem_classes": [
      "amd_oct_pwdr",
      "amd_areds_simplified_pwdr",
      "amd_referable_screening_pwdr"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.85_accuracy",
      "forward_operator": "amd_oct_pwdr_forward",
      "input_format": "macular_oct_volume_with_amd_label",
      "omega": {
        "H": 512,
        "SNR_dB": 30,
        "W": 512,
        "Z": 128,
        "axial_resolution_um": 5,
        "lateral_resolution_um": 15,
        "macular_centration": 0.0,
        "manual_grader_inter_rater_kappa": 0.0,
        "media_opacity": 0.0,
        "patient_motion": 0.0,
        "shadowing_artifact": 0.0,
        "vitreous_floater_artifact": 0.0,
        "wavelength_nm": 850
      },
      "problem_class": "amd_oct_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "H": [
        128,
        1024
      ],
      "W": [
        128,
        1024
      ],
      "Z": [
        32,
        512
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Macular layer + drusen + fluid recovery from OCT with AREDS / AMD-stage categorical readout",
  "title": "Age-Related Macular Degeneration OCT Grading (PWDR)"
}

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