A quantitative evaluation of human coordination interfaces for computer assisted surgery
Bibliographic record
Abstract
Computer assisted surgery (CAS) for tumor resection can assist the surgeon in locating the tumor margin accurately via some form of guidance method. A wide array of guidance methods can be considered, including model-based visual representations, symbolic graphical interfaces, and those based on other sensory cues such as sound. Given the variety of these guidance methods, it becomes increasingly important to test and analyze guidance methods for CAS in a quantitative and context-dependent manner to determine which is most suitable for a given surgical task. In this paper, we present a novel experimental methodology and analysis framework to test candidate guidance methods for CAS. Different viewpoints and stereographic, symbolic and auditory cues were tested in isolation or in combination in a set of virtual surgery experiments. A total of 28 participants were asked to circumscribe a virtual tumor with a magnetically tracked scalpel while measuring the surgical trajectory. This allowed measurement of surgical accuracy, speed, and the frequency with which the tumor margin was intersected, and enabled a quantitative comparison of guidance approaches. This study demonstrated that adding sound to pictorial guidance methods consistently improved accuracy, speed and margin intersection of the virtual surgery. However, the use of stereovision showed less benefit than expected. While guidance based on a combination of symbolic and pictorial cues enhanced accuracy, we found that speed could be substantially impaired. These studies demonstrate that optimal guidance combinations exist which would not be apparent by studying individual guidance methods in isolation. Our findings suggest that care is needed when using expensive and sometimes cumbersome virtual visualization technologies for CAS, and that simpler, non-stereo presentation may be sufficient for specific surgical tasks.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".