Virtual reality-enhanced ultrasound guidance: A novel technique for intracardiac interventions
Bibliographic record
Abstract
Cardiopulmonary bypass surgery, although a highly invasive interventional approach leading to numerous complications, is still the most common therapy option for treating many forms of cardiac disease. We are currently engaged in a project designed to replace many bypass surgeries with less traumatic, minimally invasive intracardiac therapies. This project combines real-time intra-operative echocardiography with a virtual reality environment providing the surgeon with a broad range of valuable information. Pre-operative images, electrophysiological data, positions of magnetically tracked surgical instruments, and dynamic surgical target representations are among the data that can be presented to the surgeon to augment intra-operative ultrasound images. This augmented reality system is applicable to procedures such as mitral valve replacement and atrial septal defect repair, as well as ablation therapies for treatment of atrial fibrillation. Our goal is to develop a robust augmented reality system that will improve the efficacy of intracardiac treatments and broaden the range of cardiac surgeries that can be performed in a minimally invasive manner. This paper provides an overview of our interventional system and specific experiments that assess its pre-clinical performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".