Approximating femoral neck bone mineral density from hand, knee, and pelvis X-rays using deep learning
Bibliographic record
Abstract
Background: A tool trained to learn the complex features of bone and soft tissue attenuation to estimate bone mineral density (BMD) at the femoral neck from standard hand, knee, and pelvis X-rays has the potential to opportunistically screen for low BMD in individuals that undergo such X-rays for any clinical indication, which in turn could empower patients and their providers to initiate preventative treatment. Methods: A retrospective study of the Osteoarthritis Initiative (OAI) dataset consisting of hand, knee, and pelvis X-rays and corresponding dual-energy X-ray absorptiometry (DXA)-derived femoral neck BMD (examinations done between 2008 to 2010) from 553 unique patients with osteoarthritis (OA) (51% male), aged between 48 to 83 years old. Participants were divided into training and test splits using a stratified random sampling procedure to ensure equal distribution of sex and age decade. A deep convolutional neural network (CNN) was trained to learn visual features from raw X-ray images, which were then combined with sex and age of the patients to estimate their femoral neck BMD. Agreement between methods at estimating BMD was assessed with Passing-Bablok regression and Bland-Altman analyses. Agreement between methods at classifying low BMD (T-score <−1) was assessed using receiver operating characteristic (ROC) curve analysis. Results: Experimental results show superior performance of the deep learning (DL) model by using either hand, knee, and pelvis X-rays, compared to baseline models, and achieved sensitivities and specificities >75% in both females and in males. It is also shown that both the X-ray and co-variate data equally contribute to the model performance. Conclusions: These results indicate that low BMD at the femoral neck can be opportunistically screened from routinely acquired X-rays of the hand, knee, or pelvis, i.e., even when the femoral neck is not included in the field of view.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".