Toward the design of a novel surgeon-computer interface using image processing of surgical tools in minimally invasive surgery
Bibliographic record
Abstract
Minimally invasive surgery (or key-hole surgery) is an alternative to open surgery has been gaining popularity among patients and health delivery systems. In general, to view the surgical site, an endoscope is inserted into the abdominal cavity though natural or artificial incision. Long stem surgical tools are also inserted through supporting incisions. The surgeon can then perform the operation by indirectly viewing the scene and manipulating the surgical tools. While viewing the monitor, the surgeon do not have any automatic access to preoperative images or patient specific data or be able to manipulate superimpose them on the viewing monitor. This paper presents a novel approach based on image processing of the surgical site and neural network framework for classifying and identifying gestures of surgical tools and classification of their motions. Seven feature quantities were selected as an input to a feed-forward neural network. Experimental analysis of the classification was carried-out for single tools and multiple tool gestures in an in-vitro setting. Through a number of trails we were able to demonstrate the feasibility of our gesture recognition approaches.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".