Bibliographic record
Abstract
INTRODUCTION: High-resolution peripheral quantitative computed tomography (HR-pQCT) provides detailed bone microarchitecture assessments, but the interpretability of its many complex parameters remains challenging. This study aimed to develop a deep learning model to estimate skeletal age from HR-pQCT scans, offering an interpretable, quantitative summary of bone health relative to chronological age. METHODS: The training dataset included 1236 adults (62.1% female) from a normative cohort, and an independent test set of 460 adults (69.3% female). HR-pQCT scans of the distal radius and tibia were acquired for all participants. Five models were trained: 2D models using a single radius (2DRad) and tibia (2DTib) slice from the middle of the scan; 3D models using full volumetric radius (3DRad) and tibia (3DTib); and a combined 2D model (2DRadTib) applying linear regression to the 2D outputs. RESULTS: = 0.85). Saliency maps revealed cortical bone was most influential in younger individuals, while both cortical and trabecular features contributed in older participants. Predicted skeletal age was strongly correlated with established HR-pQCT parameters, particularly cortical and density measures (ρ = -0.51 to 0.85), indicating the model relies on key bone features. CONCLUSION: We present a novel deep learning framework for skeletal age prediction from HR-pQCT, providing a concise and interpretable summary measure of bone health. This approach may enhance the clinical utility of HR-pQCT by improving interpretability and supporting early identification of accelerated skeletal aging. LAY SUMMARY: High-resolution peripheral quantitative computed tomography (HR-pQCT) provides detailed images of bone structure, but the volume and complexity of data can make interpretation difficult. In this study, we developed a deep learning model to estimate skeletal age from HR-pQCT scans, offering a simplified and interpretable measure of bone health. By translating complex imaging data into an age-based summary, this approach may enhance clinical use of HR-pQCT, support early identification of individuals at risk of accelerated bone loss, and improve patient understanding by providing a relatable measure of skeletal integrity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| 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 teacher head, 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".