Three-Dimensional Eye Tracking in a Surgical Scenario
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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