Usefulness of three‐dimensional imaging in a flexible endoscopic surgery platform with multi‐degrees‐of‐freedom articulating devices
Bibliographic record
Abstract
BACKGROUND AND AIMS: Recent studies on endoscopic submucosal dissection have aimed to reduce the difficulty of the procedure by using multi-degrees-of-freedom articulating devices. In this study, we evaluated the usefulness of adding three-dimensional (3D) video imaging into simulated endoscopic submucosal dissection tasks using multi-degrees-of-freedom devices. METHODS: We designed an endoscopic platform with a 3D camera and two multi-degrees-of-freedom devices. Four ex vivo bench tasks were created, and a crossover study comparing 2D and 3D conditions was conducted on 15 volunteers. In each task, performance such as procedure time and accuracy were objectively evaluated. Additionally, a comprehensive visual analogue scale questionnaire was conducted. RESULTS: In the tasks simulating submucosal flap grasping, marking, and full-area incision, the use of 3D imaging significantly improved the speed and accuracy of the multi-degrees-of-freedom device manipulation (p < .01). No significant differences were observed in the task that simulated the dissection procedure. Furthermore, it appears that the accuracy of recognizing curved surfaces may be reduced in the 3D environment. Operators reported subjective increases in recognizability and operability with the 3D camera, along with an increase in asthenopia (p < .01). CONCLUSIONS: 3D vision improves the technical accuracy of certain simulated multi-degrees-of-freedom endoscopic submucosal dissection tasks and subjectively improved operating conditions, at the cost of increased eye strain.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".