Surgical accuracy under virtual reality-enhanced ultrasound guidance: An in vitro epicardial dynamic study
Bibliographic record
Abstract
In the context of our ongoing objective to reduce morbidity associated with cardiac interventions, minimizing invasiveness has inevitably led to more limited visual access to the target tissues. To ameliorate these challenges, we provide the surgeons with a complex visualization environment that integrates interventional ultrasound imaging augmented with pre-operative anatomical models and virtual surgical instruments within a virtual reality environment. In this paper we present an in vitro study on a cardiac phantom aimed at assessing the feasibility and targeting accuracy of our surgical system in comparison to traditional ultrasound imaging for intra-operative surgical guidance. The 'therapy delivery' was modeled in the context of a blinded procedure, mimicking a closed-chest intervention. Four users navigated a tracked pointer to a target, under guidance provide by either US imaging or virtual reality-enhanced ultrasound. A 2.8 mm RMS targeting error was achieved using our novel surgical system, which is adequate from both a clinical and engineering perspective, under the inherent procedure requirements and limitations of the system.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".