Can a partial volume edge effect reduction algorithm improve the repeatability of subject-specific finite element models of femurs obtained from CT data?
Why this work is in the frame
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Bibliographic record
Abstract
The reliability of patient-specific finite element (FE) modelling is dependent on the ability to provide repeatable analyses. Differences of inter-operator generated grids can produce variability in strain and stress readings at a desired location, which are magnified at the surface of the model as a result of the partial volume edge effects (PVEEs). In this study, a new approach is introduced based on an in-house developed algorithm which adjusts the location of the model's surface nodes to a consistent predefined threshold Hounsfield unit value. Three cadaveric human femora specimens were CT scanned, and surface models were created after a semi-automatic segmentation by three different experienced operators. A FE analysis was conducted for each model, with and without applying the surface-adjustment algorithm (a total of 18 models), implementing identical boundary conditions. Maximum principal strain and stress and spatial coordinates were probed at six equivalent surface nodes from the six generated models for each of the three specimens at locations commonly utilised for experimental strain guage measurement validation. A Wilcoxon signed-ranks test was conducted to determine inter-operator variability and the impact of the PVEE-adjustment algorithm. The average inter-operator difference in stress values was significantly reduced after applying the adjustment algorithm (before: 3.32 ± 4.35 MPa, after: 1.47 ± 1.77 MPa, p = 0.025). Strain values were found to be less sensitive to inter-operative variability (p = 0.286). In summary, the new approach as presented in this study may provide a means to improve the repeatability of subject-specific FE models of bone obtained from CT data.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it