Three-Dimensional Eye Tracking in a Surgical Scenario
Bibliographic record
Abstract
INTRODUCTION: Eye tracking has been widely used in studying the eye behavior of surgeons in the past decade. Most eye-tracking data are reported in a 2-dimensional (2D) fashion, and data for describing surgeons' behaviors on stereoperception are often missed. With the introduction of stereoscopes in laparoscopic procedures, there is an increasing need for studying the depth perception of surgeons under 3D image-guided surgery. METHODS: We developed a new algorithm for the computation of convergence points in stereovision by measuring surgeons' interpupillary distance, the distance to the view target, and the difference between gaze locations of the 2 eyes. To test the feasibility of our new algorithm, we recruited 10 individuals to watch stereograms using binocular disparity and asked them to develop stereoperception using a cross-eyed viewing technique. Participants' eye motions were recorded by the Tobii eye tracker while they performed the trials. Convergence points between normal and stereo-viewing conditions were computed using the developed algorithm. RESULTS: All 10 participants were able to develop stereovision after a short period of training. During stereovision, participants' eye convergence points were 14 ± 1 cm in front of their eyes, which was significantly closer than the convergence points under the normal viewing condition (77 ± 20 cm). CONCLUSION: By applying our method of calculating convergence points using eye tracking, we were able to elicit the eye movement patterns of human operators between the normal and stereovision conditions. Knowledge from this study can be applied to the design of surgical visual systems, with the goal of improving surgical performance and patient safety.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".