Detecting human articular cartilage degeneration in its early stage with polarization-sensitive optical coherence tomography
Bibliographic record
Abstract
Detecting articular cartilage (AC) degeneration in its early stage plays a critical role in the diagnosis and treatment of osteoarthritis (OA). Polarization-sensitive optical coherence tomography (PS-OCT) is sensitive to the alteration and disruption of collagen organization that happens during OA progression. This study proposes an effective OA evaluating method based on PS-OCT imaging. A slope-based analysis is applied on the phase retardation images to segment articular cartilage into three zones along the depth direction. The boundaries and birefringence coefficients (BRCs) of each zone are quantified. Two parameters, namely phase homogeneity index (PHI) and zonal distinguishability (Dz), are further developed to quantify the fluctuation within each zone and the zone-to-zone variation of the tissue birefringence properties. The PS-OCT based evaluating method then combines PHI and Dz to provide a G PS score for the severity of OA. The proposed method is applied to human hip joint samples and the results are compared with the grading by histology images. The G PS score shows very strong statistical significance in differentiating different stages of OA. Compared to using the BRC of each zone or a single BRC for the entire depth, the G PS score shows great improvement in differentiating early-stage OA. The proposed method is shown to have great potential to be developed as a clinical tool for detecting OA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".