Quality examination of clinical DXA images for assessment of hip fracture risk by finite element modeling
Bibliographic record
Abstract
A DXA (Dual Energy X-ray Absorptiometry) based finite element model was previously developed and automated for clinical assessment of hip fracture risk caused by sideways fall. However, one remaining issue for the finite element model was that the accuracy of assessment is dependent on the quality of DXA images. Requirements on DXA images for finite element analysis are much more stringent than those for clinical densitometry examination. Therefore, the objective of this study was to apply and implement algorithms to automatically examine clinical DXA images before finite element analysis. In this study, two algorithms were implemented to work in series for DXA quality examination, they are the k-NN algorithm and average distance between neighboring points on femur contour. Geometric features of common defects in clinical im-ages were identified and quantified. After that, these features were used for training the algorithms. Clinical DXA images were acquired from St. Boniface General Hospital (SBGH) to validate the algorithms. A standalone computer program was wrapped using MATLAB packages. The computer program was tested by SBGH and the assessment accuracy of hip fracture risk was substantially improved. However, a number of minor defects have been identified in the clinical testing, which will be implemented in a new version of the computer program.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".