Free Head Movement Eye Gaze Contingent Ultrasound Interfaces for the da Vinci Surgical System
Bibliographic record
Abstract
The current practice of intraoperative ultrasound requires an assistant because the surgeon's hands are occupied with surgical tools or console instruments. This process can be tedious and prone to error. Eye gaze is a promising control modality that can help address this issue. In previous work, a novel feature-based retro-fit eye gaze tracker has been designed for the da Vinci surgical system. In this letter, leveraging the da Vinci research kit, three interfaces incorporate eye gaze, and voice recognition into the da Vinci surgical system for ultrasound control in one common framework. This letter aims to improve autonomous use of ultrasound for surgeons. Since eye gaze tracking is sensitive to head movement, a novel calibration procedure is also proposed to accommodate head motion by decomposing pupil movement into eye rotation and head motion. This ensures that the eye gaze tracking can be reliably used as a control modality. A user study (N = 20) has shown that the designed eye gaze tracker has a mean binocular accuracy of 1.98° with mean -0.92 mm horizontal and 16.83-mm vertical head movement. A preliminary user study (N = 9) has shown that eye gaze tracking for ultrasound control has the potential to improve the way surgeons interact with their instrumentation and increase surgical autonomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".