High-fidelity haptic and visual rendering for patient-specific simulation of temporal bone surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical imaging techniques provide a wealth of information for surgical preparation, but it is still often the case that surgeons are examining three-dimensional pre-operative image data as a series of two-dimensional images. With recent advances in visual computing and interactive technologies, there is much opportunity to provide surgeons an ability to actively manipulate and interpret digital image data in a surgically meaningful way. This article describes the design and initial evaluation of a virtual surgical environment that supports patient-specific simulation of temporal bone surgery using pre-operative medical image data. Computational methods are presented that enable six degree-of-freedom haptic feedback during manipulation, and that simulate virtual dissection according to the mechanical principles of orthogonal cutting and abrasive wear. A highly efficient direct volume renderer simultaneously provides high-fidelity visual feedback during surgical manipulation of the virtual anatomy. The resulting virtual surgical environment was assessed by evaluating its ability to replicate findings in the operating room, using pre-operative imaging of the same patient. Correspondences between surgical exposure, anatomical features, and the locations of pathology were readily observed when comparing intra-operative video with the simulation, indicating the predictive ability of the virtual surgical environment.
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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 | 0.001 | 0.000 |
| 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 it