Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images
Bibliographic record
Abstract
ABSTRACT There is growing interest in applying HR-pQCT to image the knee, particularly in the study of osteoarthritis, which necessitates the development and validation of novel image analysis workflows. In this work, we present and validate the first fully automated workflow for in vivo quantitative assessment of peri-articular bone density and microarchitecture in the human knee. Bone segmentation models were trained by transfer learning with a large dataset of radius and tibia images (N=2,598) and fine-tuned on a knee image dataset (N=131), atlas-based registration was used to identify medial and lateral contact surfaces, and morphological operations combined these intermediate outputs to generate peri-articular regions of interest (ROIs) for morphological analysis. Accuracy was assessed with an external validation dataset (N=131), where predicted and reference morphological parameters showed excellent correspondence (0.86≤R 2 ≤0.99), with moderate bias present in predictions of subchondral bone plate density (-80 mg HA/cm 3 ) and thickness (+0.15 mm). Precision was assessed with a triple-repeat measures dataset (N=29), where the short-term precision RMS%CV estimates ranged from 0.7% to 3.5% when rigid registration was used to synchronize ROI generation across images.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".