Volume measurement of bone erosions in magnetic resonance images of patients with rheumatoid arthritis
Bibliographic record
Abstract
The volume of bone erosions in the metacarpophalangeal joints is a radiological feature that can be used to track the progression of rheumatoid arthritis. We introduce a hybrid segmentation algorithm that combines region growing and level-set segmentation algorithms to semiautomatically measure the volume of bone erosions in magnetic resonance images. A total of 40 rheumatoid arthritis patients were included in the study. The scans of eight patients were used for training, whereas the remaining 32 scans were used to determine the accuracy, precision, and speed of the technique. The reproducibility of the semiautomated technique and that of manual segmentation was defined in terms of intraclass correlation coefficients. Both techniques were equally precise with intraclass correlation coefficient values greater than 0.9. The hybrid algorithm was highly accurate: the least squares fit between the semiautomated segmentations to those manually traced by a musculoskeletal radiologist resulted in a slope of 1.030 with an x-intercept of 1.385 mm(3) and an R(2) value of 0.923. The semiautomated technique was significantly faster than manual segmentation, which took two to four times longer to complete. Our hybrid algorithm shows promise in the quantitative assessment of radiological features of rheumatoid arthritis in a clinical setting.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".