Ultrasound Image and Augmented Reality Guidance for Off‐pump, Closed, Beating, Intracardiac Surgery
Bibliographic record
Abstract
Our project is the reintroduction of off-pump intracardiac surgery using the Universal Cardiac Introducer (UCI) for safe intracardiac access. The purpose of this study was to evaluate multimodality visualization using three ultrasound modalities and ultrasound augmented with virtual reality. Image guidance was tested on implanting a mitral valve prosthesis via the UCI in 12 pigs. Initially, two-dimensional (2-D) transesophageal echocardiography (TEE) ultrasound, intravascular ultrasound (intracardiac echocardiography [ICE]), and three-dimensional (3-D) epicardial ultrasound were utilized. Ultrasound augmented with virtual reality was used in the last three experiments. A 2-D TEE assisted navigating the prosthesis into the orifice. Positioning was not intuitive and required trial and error method. A 3-D epicardial ultrasound allowed positioning of the valve into the orifice. Positioning of the clip was difficult because of artifacts with multiple reflections and shadowing. Augmented reality displayed the entire prosthesis and the tools without artifacts; provided intuitive information on navigation, positioning, and orientation of tools; and improved significantly image guidance and surgical skill. Augmented virtual reality, with tracked 2-D or 3-D ultrasound imaging, provides guidance that can effectively substitute for direct vision during beating heart intracardiac surgery.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".