Statistical comparison between a real-time model and a FEM counterpart for visualization of breast phantom deformation during palpation
Bibliographic record
Abstract
In developing a Virtual Reality simulation for learning breast palpation, one of critical aspects is real-time visualization of breast phantom deformation during palpation. Available models are either offline ones using Finite Element Method (FEM) analysis with considering some material parameters of deformable objects; or real-time ones with difficulties of balancing between this consideration and realistic visualization. For visual perception of breast phantom deformation, we used a real-time model with an inside pressure to keep the volume of the breast phantom constant. On a meshed breast phantom, we compared the displacements of vertices governed by the real-time model with those governed by its FEM counterpart for four different distributions of contact force. To satisfy visual perception of breast phantom deformation, we examined the comparison by utilizing the statistical methods of ANOVA and Bland and Altman agreement. The results revealed that the displacements of vertices governed by the real-time model are in agreement with those by its FEM counterpart for each distribution of contact force. This observation indicates the potential of our real-time model for visualizing breast phantom deformation during palpation.
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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 | 0.005 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".