Evaluation of an Implantable Electromagnetic Microsensor for Computer-Assisted Surgery
Bibliographic record
Abstract
Computer-assisted surgical navigation systems require a high tracking accuracy while not occupying much space. Currently, the size of the electromagnetic tracking sensors in use can be distracting to the surgeon. An electromagnetic microsensors (1.04 mm x 7.9 mm) has been developed to promote seamless integration within surgical workspace. This study evaluates the performance accuracy of this microsensors, to determine the suitability for the clinical setting. A series of experiments were performed to determine the sensor’s accuracy in a controlled environment, in the presence of ferromagnetic materials, and while an electrocautery is used. The electromagnetic sensor was compared to an optical tracking ground truth to determine the tracking error. Initial tests in the simulated surgical environment demonstrate that the microsensor maintains a below 1 mm error and minimal jitter error when 15 cm away from the field generator. However, accuracy decreases in the presence of ferromagnetic materials and an electrocautery, especially if the electrocautery is in coagulation mode which can result in sensor damage. These findings highlight the sensor’s potential in surgical navigation, while also indicating the need for further improvements to ensure functionality in the presence of surgical tools and in varying operational conditions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".