Virtual Tape Measure for the Operating Microscope: System Specifications and Performance Evaluation
Bibliographic record
Abstract
Objective: The Virtual Tape Measure for the Operating Microscope (VTMOM) was created to assist surgeons in making accurate 3D measurements of anatomical structures seen in the surgical field under the operating microscope. The VTMOM employs augmented reality techniques by combining stereoscopic video images with stereoscopic computer graphics, and functions by reiving on an operator's ability to align a 3D graphic pointer, which serves as the end-point of the virtual tape measure, with designated locations on the anatomical structure being measured. The VTMOM was evaluated for its baseline and application performances as well as its application efficacy.Methods: Baseline performance was determined by measuring the mean error (bias) and standard deviation of error (imprecision) in measurements of non-anatomical objects. Application performance was determined by comparing the error in measuring the dimensions of aneurysm models with and without the VTMOM. Application efficacy was determined by comparing the error in selecting the appropriate aneurysm clip size with and without the VTMOM.Results: Baseline performance indicated a bias of 0.3 mm and an imprecision of 0.6 mm. Application bias was 3.8 mm and imprecision was 2.8 mm for aneurysm diameter. The VTMOM did not improve aneurysm clip size selection accuracy.Discussion and Conclusion: The VTMOM is a potentially accurate tool for use under the operating microscope. However, its performance when measuring anatomical objects is highly dependent on complex visual features of the object surfaces.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".