Standardized accuracy assessment of the calypso wireless transponder tracking system
Bibliographic record
Abstract
Electromagnetic (EM) tracking allows localization of small EM sensors in a magnetic field of known geometry without line-of-sight. However, this technique requires a cable connection to the tracked object. A wireless alternative based on magnetic fields, referred to as transponder tracking, has been proposed by several authors. Although most of the transponder tracking systems are still in an early stage of development and not ready for clinical use yet, Varian Medical Systems Inc. (Palo Alto, California, USA) presented the Calypso system for tumor tracking in radiation therapy which includes transponder technology. But it has not been used for computer-assisted interventions (CAI) in general or been assessed for accuracy in a standardized manner, so far. In this study, we apply a standardized assessment protocol presented by Hummel et al (2005 Med. Phys. 32 2371-9) to the Calypso system for the first time. The results show that transponder tracking with the Calypso system provides a precision and accuracy below 1 mm in ideal clinical environments, which is comparable with other EM tracking systems. Similar to other systems the tracking accuracy was affected by metallic distortion, which led to errors of up to 3.2 mm. The potential of the wireless transponder tracking technology for use in many future CAI applications can be regarded as extremely high.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".