Multi-modal Framework for Image-guided Trans-oral Surgery with Intraoperative Imaging and Deformation Modeling
Bibliographic record
Abstract
Treatment of throat cancers have improved due to minimally-invasive trans-oral approaches. Surgeons rely on preoperative imaging to guide their resection; however, large tissue deformations occur during trans-oral procedures due to placement of necessary retractors and laryngoscopes which hinders the surgeon’s ability to accurately assess tumor extent and location of critical structures. We propose an image-guided framework utilizing intraoperative imaging and deformation modeling to improve surgeon accuracy and confidence. A CT-compatible laryngoscopy system previously developed was evaluated in this framework. Intraoperative images were acquired during laryngoscopy; force-sensing capabilities were enabled in the laryngoscope; and tracking of the scope and anatomic features was trialed. Tissue deformation and displacement were quantified and determined to be extensive, with values3in airway volume change. Surgical navigation using intraoperative imaging and tracking was evaluated. Preliminary assessment of deformation modeling showed potential to supplement intraoperative imaging. Future work will involve streamlined integration of the components of this framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".