3D Graphical Rendering of Localized Lumps and Arteries for Robotic Assisted MIS
Bibliographic record
Abstract
Detection of hard inclusions within soft tissue in robotic assisted minimally invasive surgery (MIS), also referred to as laparoscopic surgery, is of great importance, both in clinical and surgical applications. In clinical applications, surgeons need to detect and precisely identify the location and size of all growths, whether cancerous or benign, that are present within surrounding tissue in order to assess the extent and nature of any future treatment plan. In surgical applications, when any solid matter is being removed, it is important to avoid accidental injury to surrounding tissues and blood vessels since, were this to occur, it could then necessitate the need to resort to open surgery. The present study is aimed at developing a three-dimensional tactile display that provides palpation capability to any surgeon performing robotic assisted MIS. The information is collected from two force sensor/pressure matrices and processed with a new algorithm and graphically rendered. Consequently, the surgeon can determine the presence, location, and the size of any hidden superficial tumor/artery by grasping the target tissue in a quasi-dynamic way. The developed algorithm is presented, and the results for various configurations of embedded tumor/arteries inside the tissue are compared with those of the finite element analysis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".