Video Processing to Locate the Tooltip Position in Surgical Eye–Hand Coordination Tasks
Bibliographic record
Abstract
INTRODUCTION: Trajectories of surgical instruments in laparoscopic surgery contain rich information about surgeons' performance. In a simulation environment, instrument trajectories can be taken by motion sensors attached to the instruments. This method is not accepted by surgeons working in the operating room due to safety concerns. In this study, a novel approach of acquiring instrument trajectories from surgical videos is reported. METHODS: A total of 12 surgical videos were obtained for this study. The videos were captured during simulated laparoscopic procedures where subjects were required to pick up and transport an object over 3 different targets using a laparoscopic grasper. An algorithm was developed to allow the computer to identify the tip of the grasper on each frame of video, and then compute the trajectories of grasper movement. RESULTS: The newly developed algorithm successfully identified tool trajectories from all 12 surgical videos. To validate the accuracy of this algorithm, the location of the tooltip in these videos were also manually labeled. The rate of accurate matching between these 2 methods was 98.4% of all video frames. DISCUSSION: Identifying tool movement from surgical videos creates an effective way to track instrument trajectories. This builds up the foundation for assessing psychomotor performance of surgeons in the operating room without jeopardizing 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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".