Knee / Hip Osteoarthritis Kellgren-Lawrence Grading (PWDR) L1-527

Medical ImagingJoint-space-narrowing + osteophyte recovery from radiograph with KL-grade categorical readoutδ=3 · standardL_DAG = 4.6📋 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.

To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.

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Forward model E

S t a g e 1 (L 1 - 031) : r a d i o g r a p h \to J S W, os t eo p h y t e_{c} o u n t, s u b c h o n d r a l_{s} c l er os i s, d e f or mi t y . S t a g e 2 : K L_{g} r a d e = s t e p (f e a t u r es a tt h r es h o l d s p er K e l l g r e n - L a w r e n ce 1957 a tl a s) .

Knee / Hip OA KL Grading (PWDR): wraps L1-031 with Kellgren-Lawrence 1957 grading rules. Stage 1 (analytical, from L1-031): from weight-bearing radiograph, recover joint-space width, osteophyte morphology, subchondral bone density, joint-line obliquity. Stage 2 (deterministic threshold): apply KL atlas decision tree; OARSI compartment grade overlay if requested. Difficulty tier delta = 3.

L-DAG

L.xray_source -> L.attenuation_projection -> L.joint_space_measurement -> L.osteophyte_detection -> L.kl_grade_classifier -> int.spatial

L.xray_sourceL.attenuation_projectionL.joint_space_measurementL.osteophyte_detectionL.kl_grade_classifierint.spatial

Well-posedness W

Existence:: true
Uniqueness:: conditional
Stability:: conditional
κ:: 60

Existence inherited from L1-031. Uniqueness conditional on consistent weight-bearing positioning. Stability dominated by manual_grader_inter_rater_kappa (~0.55-0.75 typical). Joint Hadamard well-posedness established by Kellgren-Lawrence 1957 (foundational), Altman 1986 (ACR criteria), Tiulpin 2018 (deep learning KL grading benchmark), Norman 2019 (DeepKnee multi-task).

Solvability C

Solver class:: linear-operator + image-segmentation [JSW measurement + osteophyte detection] + categorical-readout [KL atlas classifier] | end-to-end deep neural [DeepKnee, Tiulpin OA-CNN]
Convergence rate q:: 2
Complexity:: O(H * W) for stage 1; O(1) for stage 2

Specs (0)

No L2 specs registered yet for this principle.