Statistical Shape and Bone Property Models of Clinical Populations as the Foundation for Biomechanical Surgical Planning: Application to Shoulder Arthroplasty
Bibliographic record
Abstract
This work developed, validated, and compared statistical shape, statistical intensity, and statistical shape and intensity models (SSMs, SIMs, SSIMs) of scapulae from a clinical population. SSMs efficiently describe bone shape variation while SIMs describe bone material property variation, and SSIM's combine description of both variables. This work establishes these models' efficacy and whether they can be used in surgical planning. Models were developed using shoulder arthroplasty data of patients with bone erosion, which is challenging to treat and would benefit from improved surgical planning. Models were created using previously validated nonrigid registration and material property assignment processes that were optimized for scapula characteristics. The models were assessed using standard metrics, anatomical measurements, and correlation analyses. The SSM and SIM specificity and generalization error metrics were 3.4 mm and <1 mm and 184 HU and 156 HU, respectively. The SSIM did not achieve the same level of performance as the SSM and SIM in this study (e.g., shape generalization: SSIM-2.2 mm versus SSM-<1 mm). Anatomical correlation analysis showed that the SSM more effectively and efficiently described shape variation compared to the SSIM. The SSM and SIM modes of variation were not strongly correlated (e.g., rmax = 0.56 for modes explaining ≤2.1% of variance). The SSIM is outperformed by the SSM and SIM and the latter two are not strongly correlated; therefore, using the SSM and SIM in conjunction will generate synthetic bone models with realistic characteristics and thus can be used for biomechanical surgical planning applications.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".