Inside-Out, Real-Time Tracking Solution for Enhanced Laparoscopic Surgery
Bibliographic record
Abstract
Laparoscopy is a minimally invasive surgery that has many benefits including reduced recovery times and infection risks. However, the inability of surgeons to observe the laparoscope’s location within a patient’s body makes it a challenging technique to perform, especially for trainees. Current surgical instrument tracking systems implement an outside-in approach, employing cameras within the operating room to track the movement of markers located on the laparoscope. This solution is impractical as the markers are often bulky and interfere with the surgeon’s ability to maneuver the laparoscope. Recent developments in miniaturized camera technology indicate potential for the development of a highly accurate tracking system that minimally interferes with a surgeon’s operation of the laparoscope. This paper investigates a novel application of the inside-out technique to estimate the pose of a laparoscope over six degrees of freedom, in real-time. A camera is attached to the laparoscope and detects passive markers located within the surgical environment. The Harris-Stephens algorithm is implemented to automate monocular triangulation for camera pose estimation. Preliminary testing proved this inside-out approach to be accurate on the order of millimeters, precise, and compact, thus providing a more user-friendly alternative to current technologies.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".