Platform for intraoperative analysis of video streams
Bibliographic record
Abstract
Interactive, image-guided surgery (IIGS) has proven to increase the specificity of a variety of surgical procedures. However, current IIGS systems do not compensate for changes that occur intraoperatively and are not reflected in preoperative tomograms. Endoscopes and intraoperative ultrasound, used in minimally invasive surgery, provide real-time (RT) information in a surgical setting. Combining the information from RT imaging modalities with traditional IIGS techniques will further increase surgical specificity by providing enhanced anatomical information. In order to merge these techniques and obtain quantitative data from RT imaging modalities, a platform was developed to allow both the display and processing of video streams in RT. Using a Bandit-II CV frame grabber board (Coreco Imaging, St. Laurent, Quebec) and the associated library API, a dynamic link library was created in Microsoft Visual C++ 6.0 such that the platform could be incorporated into the IIGS system developed at Vanderbilt University. Performance characterization, using two relatively inexpensive host computers, has shown the platform capable of performing simple image processing operations on frames captured from a CCD camera and displaying the processed video data at near RT rates both independent of and while running the IIGS system.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".