Transcatheter tricuspid valve implantation with LuX-Valve utilizing a novel patient-specific virtual and physical simulator: a case report
Bibliographic record
Abstract
Background: The rise of transcatheter tricuspid valve implantation (TTVI) therapies represents a major advancement for high-risk patients with severe tricuspid valve regurgitation, offering a safer, minimally invasive alternative to open-heart surgery. However, the low volume of procedures and training highlights an urgent need for skills development and pre-procedural preparation, which simulation can address by enhancing learning and expanding treatment availability. Case summary: An 87-year-old woman with permanent atrial fibrillation and symptomatic severe functional tricuspid regurgitation underwent a transcatheter tricuspid valve replacement with the LuX-Valve system. We developed a novel patient-specific virtual reality simulator, combining virtual and physical simulations, to enhance training and education for TTVI. This system utilizes high-resolution computed tomography images, machine learning algorithms, and a video game engine to recreate realistic procedural environments. We performed a safe intervention following the simulation session, achieving successful clinical outcomes in the patient. Discussion: The developed platform is the first to propose a patient-specific hybrid simulation for TTVI engaging both interventional and imaging cardiologists. The simulator's potential to improve clinical and safety outcomes warrants further evaluation through specifically designed comparative studies.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".