Comparison of clinical‐CT segmentation techniques for measuring subchondral bone cyst volume in glenohumeral osteoarthritis
Bibliographic record
Abstract
Abstract Purpose This study aimed to assess the accuracy and reproducibility of four common segmentation techniques measuring subchondral bone cyst volume in clinical‐CT scans of glenohumeral OA patients. Methods Ten humeral head osteotomies collected from cystic OA patients, having undergone total shoulder arthroplasty, were scanned within a micro‐CT scanner, and corresponding preoperative clinical‐CT scans were gathered. Cyst volumes were measured manually in micro‐CT and served as a reference standard (n = 13). Respective cyst volumes were measured on the clinical‐CT scans by two independent graders using four segmentation techniques: Qualitative, Edge Detection, Region Growing, and Thresholding. Cyst volume measured in micro‐CT was compared to the different clinical‐CT techniques using linear regression and Bland–Altman analysis. Reproducibility of each technique was assessed using intraclass correlation coefficient (ICC). Results Each technique outputted lower volumes on average than the reference standard (‐0.24 to ‐3.99 mm3). All linear regression slopes and intercepts were not significantly different than 1 and 0, respectively (p < 0.05). Cyst volumes measured using Qualitative and Edge Detection techniques had the highest overall agreement with reference micro‐CT volumes (mean discrepancy: 0.24, 0.92 mm3). These techniques showed good to excellent reproducibility between graders. Conclusions Qualitative and Edge Detection techniques were found to accurately and reproducibly measure subchondral cyst volume in clinical‐CT. These findings provide evidence that clinical‐CT may accurately gauge glenohumeral cystic presence, which may be useful for disease monitoring and preoperative planning. Level of evidence Retrospective cohort Level 3 study.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".