L1 ⚛ L1-523 ⊙ Testnet

Pneumothorax Detection from Chest X-ray (PWDR)

Medical Imaging · Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout

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⬇ principle.md ⬇ principle.json

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⬇ L1-523.md ⬇ L1-523.json

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You are helping me submit a MODIFICATION of L1-523 (Pneumothorax Detection from Chest X-ray (PWDR)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-523.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-523.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.severity_threshold_classifier` `L.light_index_estimation` `L.pleural_line_detection` ∫µ dl I₀(E) x,    measurements ~ Poisson(αy)

world state x (1D pneumothorax severity)

→

X-ray source I₀(E)

→

Attenuation projection ∫µ dl

→

L · pleural line detection `L.pleural_line_detection`

→

L · light index estimation `L.light_index_estimation`

→

L · severity threshold classifier `L.severity_threshold_classifier`

→

Spatial integration ∫_A dA

→

observation y (x ray detector)

Noise: Poisson counting noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-523.md

# ⚛ L1 Principle — Pneumothorax Detection from Chest X-ray (PWDR) **ID:** `L1-523` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D pneumothorax severity > **📡 Carrier:** x_ray · **🌫 Noise:** poisson > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41552306 --- ## 🧠 1. Introduction **Pneumothorax Detection from Chest X-ray (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D pneumothorax severity** space, within the **Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout** sub-domain of **Medical Imaging**. Measurements consist of X-ray photons transmitted through (or scattered by) the sample via a **radiograph with pneumothorax threshold** sensing mechanism. The forward operator applies, in order: polyenergetic X-ray emission spectrum; line-integral projection through an attenuation map; L · pleural line detection operator; L · light index estimation operator; L · severity threshold classifier operator; pixel-level spatial averaging on the detector. Observations are corrupted by Poisson counting noise. Existence inherited from L1-031. Uniqueness conditional on pneumothorax visibility; small pneumothoraces near lung apex can be missed in supine acquisitions (sensitivity drops 30-50% supine vs upright). Stability dominated by skin_fold_pseudo_pneumothorax and bullous_disease_confounders (the dominant false-positive sources in clinical practice). Joint Hadamard well-posedness for the pneumothorax-detection forward established by Light 1985 (foundational Light index), Collins 1995 (Collins method), Rhea 1982 (Rhea method), Roberts 2014 (ATLS guideline), Rajpurkar 2017 (CheXNet 14-pathology benchmark), Tang 2020 (ChestX-ray14). ## ⚙ 2. Forward Model Physical chain: **x** → X-ray source → Attenuation projection → L · pleural line detection → L · light index estimation → L · severity threshold classifier → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.severity_threshold_classifier` `L.light_index_estimation` `L.pleural_line_detection` ∫µ dl I₀(E) x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.xray_source` | Polyenergetic x-ray emission spectrum | | `L.attenuation_projection` | Line-integral projection through an attenuation map | | `L.pleural_line_detection` | L · pleural line detection operator | | `L.light_index_estimation` | L · light index estimation operator | | `L.severity_threshold_classifier` | L · severity threshold classifier operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout | | Carrier | x_ray | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_pneumothorax_severity | | Noise model | poisson | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 4.6 | ## 📡 4. Measurement Model Existence inherited from L1-031. Uniqueness conditional on pneumothorax visibility; small pneumothoraces near lung apex can be missed in supine acquisitions (sensitivity drops 30-50% supine vs upright). Stability dominated by skin_fold_pseudo_pneumothorax and bullous_disease_confounders (the dominant false-positive sources in clinical practice). Joint Hadamard well-posedness for the pneumothorax-detection forward established by Light 1985 (foundational Light index), Collins 1995 (Collins method), Rhea 1982 (Rhea method), Roberts 2014 (ATLS guideline), Rajpurkar 2017 (CheXNet 14-pathology benchmark), Tang 2020 (ChestX-ray14). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | sensitivity_for_tension | ## 📏 5. Operating Range (Ω) **Center problem class:** `pneumothorax_cxr_pwdr` · **Forward operator:** `cxr_pneumothorax_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 2500 | | W | px | 3000 | | Kvp | — | 110 | | Mas | — | 4 | | Snr db | dB | 30 | | Pa or ap | — | PA | | Rotation artifact | — | 0 | | Pixel resolution um | µm | 100 | | Bullous disease confounders | — | 0 | | Skin fold pseudo pneumothorax | — | 0 | | Supine vs upright acquisition | — | 0 | | Bilateral severity disagreement | — | 0 | | Expiratory vs inspiratory state | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 1024 – 4096 | | W | px | 1024 – 4096 | | Kvp | — | 70 – 130 | | Pixel resolution um | µm | 50 – 300 | ## 🎯 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 = `60` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xc106d9e5bdfa93eab31015d06783333bf6ad36cd2f3fdacf5a5bb8651514595e` - **Chain tx hash:** `0x5039f7d7d1346e2637d1029f0ef530f78ef5d358ebaec159a92a983bcd1030da` - **Chain block:** `41552306` --- ## File Mapping This bundle consists of: `L1-523.md`, `L1-523.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-523.md` | Source of truth — edit this | Human or LLM | | `L1-523.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 `L1-523`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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📋 JSON metadata

{
  "artifact_id": "L1-523",
  "chain_block": 41552306,
  "chain_hash": "0xc106d9e5bdfa93eab31015d06783333bf6ad36cd2f3fdacf5a5bb8651514595e",
  "chain_tx_hash": "0x5039f7d7d1346e2637d1029f0ef530f78ef5d358ebaec159a92a983bcd1030da",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 60,
    "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 L1-031. Uniqueness conditional on pneumothorax visibility; small pneumothoraces near lung apex can be missed in supine acquisitions (sensitivity drops 30-50% supine vs upright). Stability dominated by skin_fold_pseudo_pneumothorax and bullous_disease_confounders (the dominant false-positive sources in clinical practice). Joint Hadamard well-posedness for the pneumothorax-detection forward established by Light 1985 (foundational Light index), Collins 1995 (Collins method), Rhea 1982 (Rhea method), Roberts 2014 (ATLS guideline), Rajpurkar 2017 (CheXNet 14-pathology benchmark), Tang 2020 (ChestX-ray14).",
    "secondary": "sensitivity_for_tension"
  },
  "physics_fingerprint": {
    "L_DAG": 4.6,
    "carrier": "x_ray",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "spatial",
    "noise_model": "poisson",
    "primitives": [
      "L.xray_source",
      "L.attenuation_projection",
      "L.pleural_line_detection",
      "L.light_index_estimation",
      "L.severity_threshold_classifier",
      "int.spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "radiograph_with_pneumothorax_threshold",
    "solution_space": "1D_pneumothorax_severity",
    "sub_domain": "Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout",
    "title": "Pneumothorax Detection from Chest X-ray (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "cxr_pneumothorax_pwdr_forward",
      "cxr_pneumothorax_with_lateral_pwdr_forward",
      "cxr_pneumothorax_supine_pwdr_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "pixel_resolution_um",
      "kVp",
      "mAs",
      "PA_or_AP",
      "SNR_dB",
      "rotation_artifact",
      "expiratory_vs_inspiratory_state",
      "supine_vs_upright_acquisition",
      "skin_fold_pseudo_pneumothorax",
      "bullous_disease_confounders",
      "bilateral_severity_disagreement"
    ],
    "allowed_problem_classes": [
      "pneumothorax_cxr_pwdr",
      "pneumothorax_severity_grading_pwdr",
      "tension_pneumothorax_detection_pwdr"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.85_accuracy",
      "forward_operator": "cxr_pneumothorax_pwdr_forward",
      "input_format": "frontal_cxr_with_severity_label",
      "omega": {
        "H": 2500,
        "PA_or_AP": "PA",
        "SNR_dB": 30,
        "W": 3000,
        "bilateral_severity_disagreement": 0.0,
        "bullous_disease_confounders": 0.0,
        "expiratory_vs_inspiratory_state": 0.0,
        "kVp": 110,
        "mAs": 4,
        "pixel_resolution_um": 100,
        "rotation_artifact": 0.0,
        "skin_fold_pseudo_pneumothorax": 0.0,
        "supine_vs_upright_acquisition": 0.0
      },
      "problem_class": "pneumothorax_cxr_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "H": [
        1024,
        4096
      ],
      "W": [
        1024,
        4096
      ],
      "kVp": [
        70,
        130
      ],
      "pixel_resolution_um": [
        50,
        300
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Pleural-line and lung-edge recovery from chest radiograph with pneumothorax categorical readout",
  "title": "Pneumothorax Detection from Chest X-ray (PWDR)"
}

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