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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".