Assessment of Local Pelvic Bone Volumetric Density and Cortical Thickness Using Multi-Energy Bi-Planar Radiography
Bibliographic record
Abstract
Objective: This study presents a method to determine the volumetric density of pelvic bone and cortical bone thickness along critical regions of the S2 alar-iliac (S2AI) screw trajectory using bi-planar multi-energy X-ray (BMEX).Methods: Simulated BMEXs were generated from CT data from eight patients, with coordinate matching linking pixels to voxel density values.This dataset included pixel attenuation values, coordinates as independent variables, and voxel attenuation values (Hounsfield Units, HU) as the dependent variable for training a random forest regressor model.Results: The trained model revealed adequate trabecular bone density prediction (root mean square error: 32.8 mg/cm 3 ) and cortical thickness accuracy (error 1.2 mm).Trabecular bone showed a minor tendency for density overestimation with a maximum difference of 83 HU, while cortical bone exhibited an underestimation of up to 118 HU.Conclusions: The improved prediction of bone density and the capability to estimate cortical bone thickness signify a significant advancement towards a comprehensive modality for predicting bone quality in implant placement planning.
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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 | 0.001 | 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 | 0.000 |
| 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 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".