Augmented reality image guidance during off-pump mitral valve replacement through the guiraudon universal cardiac introducer.
Bibliographic record
Abstract
OBJECTIVE: : We report our experience with ultrasound augmented reality (US-AR) guidance for mitral valve prosthesis (MVP) implantation in the pig using off-pump, closed, beating intracardiac access through the Guiraudon Universal Cardiac Introducer attached to the left atrial appendage. METHODS: : Before testing US-AR guidance, a feasibility pilot study on nine pigs was performed using US alone. US-AR guidance, tested on a heart phantom, was subsequently used in three pigs (∼65 kg) using a tracked transesophageal echocardiography probe, augmented with registration of a 3D computed tomography scan, and virtual representation of the MVP and clip-delivering tool (Clipper); three pigs were used to test feature-based registration. RESULTS: : Navigation of the MVP was facilitated by the 3D anatomic display. AR displayed the MVP and the Clipper within the Atamai Viewer, with excellent accuracy for tool placement. Positioning the Clipper was hampered by the design of the MVP holder and Clipper. These limitations were well displayed by AR, which provided guidance for improved design of tools. CONCLUSIONS: : US-AR provided informative image guidance. It documented the flaws of the current implantation technology. This information could not be obtained by any other method of evaluation. These evaluations provided guidance for designing an integrated tool: combining an unobtrusive valve holder that allows the MVP to function properly as soon as positioned, and an anchoring system, with clips that can be released one at a time, and retracted if necessary, for optimal results. The portability of Real-time US-AR may prove to be the ideal practical image guidance system for all closed intracardiac interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".