Intelligent pointer in computer assisted surgery-design and feasibility
Bibliographic record
Abstract
We present a solution combining the newest technologies in computer vision and preoperativeiy planned surgical intervention to enhanced the efficiency of complex surgery. There have been many solutions proposed to use computer vision in the operating room, but they often depend on annoying and expensive components such as head mounted display (HMD). These systems have proven to be of weak precision and, due to their weight and size, they tend to be really disturbing if worn during long period of time. The goal of this work is to demonstrate the possibility of a system projecting directly on the patient, in real time, during the surgery. The main advantage is to keep the attention of the surgeon focused directly on his patient at all times. The information that can be added to the scene, due to the absence of HMD is evidently restricted to 2D since only one image is projected on the patient (skin, bone, surgery linen etc.) instead of two images for the right and left eyes with HMD, but by using an intelligent pointer to highlight important zones in the line of sight of the surgeon, 3D information can be inferred. This system actually transforms the patient body itself into a visual data source for the surgeon.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".