Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality
Bibliographic record
Abstract
Abstract Background Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk. Methods In the VERTE-X cohort (n=10,341, age 40 or older; derivation set) and KURE cohort (n=3,517; age 65 or older; external test set), predicted age difference was defined as estimated spine age minus chronological age. The primary outcome was incident fracture. Secondary outcomes included morphologic vertebral fracture, osteoporosis, and incident mortality. Results Incidence of overall fracture was 20.5/1000 and 21.0/1000 person-years (median follow-up 5.4 and 6.6 years) in VERTE-X and KURE, respectively. Spine age discriminated prevalent vertebral fractures and osteoporosis better than chronological age. Higher predicted age difference (PAD) was associated with greater risk of overall (VERTE-x: adjusted HR [aHR] 1.71; KURE: aHR 1.22 per 1 standard deviation [SD] increment), vertebral (aHR 1.55 and 1.34), and non-vertebral fractures (aHR 1.89 and 1.15, p<0.05 for all), independent of chronological age and prevalent vertebral fracture. FRAX hip fracture probabilities based on spine age improved discrimination for incident hip fracture over chronological age (AUROC 0.83 vs. 0.78, p=0.027). Shorter height, lower femoral neck BMD, diabetes, vertebral fractures, and surgical prosthesis were associated with higher predicted age difference, explaining 40% of variance. In the external test set, higher predicted age difference was associated with greater risk of mortality (aHR 1.31 per 1 SD increment, p=0.001), independent of covariates. Conclusion Spine age estimated from lateral spine radiographs and DXA VFA enhanced fracture risk assessment and mortality prediction in adults. Key Points Spine age estimated from lateral spine radiographs and DXA VFA using deep learning outperformed chronological age in discriminating morphologic vertebral fracture and osteoporosis. Higher predicted age difference (predicted spine age minus chronological age) was associated with greater risk of overall, vertebral, and non-vertebral incident fracture, independent of covariates. Male sex, lower height, lower femoral neck BMD, diabetes mellitus, morphologic vertebral fractures, and surgical prosthesis were correlated with higher predicted age difference, explaining up to 40% variance. Higher predicted age difference was associated with greater risk of mortality, independent of chronological age, sex, prevalent morphologic vertebral, fracture, and clinical biomarkers related to mortality including serum albumin, hemoglobin, and creatinine.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".