Current sensing for navigated electrosurgery: proof of concept
Bibliographic record
Abstract
PURPOSE: Tracked power-tools are routinely used in computer-assisted intervention and surgical systems. In order to properly perform temporal and spatial monitoring of the tracked tool with the navigation system, it is important to know when the tool, such as an electrosurgical cauterizer, is being activated during surgery. We have developed a general purpose current sensor that can be augmented to tracked surgical devices in order to inform the surgeon and the navigation system when the tool is activated. METHODS: Two non-invasive AC current sensors, two peak detector circuits, one voltage comparator circuit, and a microcontroller were used to detect when an electrosurgical cauterizer is being powered on and differentiate between the cut and coagulation modes. The system was tested by cauterizing various substances at varied power ratings. RESULTS: By comparing the ratio of amplitudes as well as the frequencies of the signals, the current sensing system is able to differentiate between on/off, cut/coagulation, as well as when cauterizer tissue. DISCUSSION: The current sensing system is able to detect when the cauterizer is being powered on and can differentiate between monopolar cut and coagulation modes. CONCLUSION: This system shows promise for detecting when the cauterizer is being powered on and in the future could be integrated with a navigation system in order to easily temporally monitor the electrosurgical tool.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".