Computational simulation model of transcatheter edge-to-edge mitral valve repair: a proof-of-concept study
Bibliographic record
Abstract
AIMS: As transcatheter mitral valve (MV) interventions are expanding and more device types and sizes become available, a tool supporting operators in pre-procedural planning and the clinical decision-making process is highly desirable. We sought to develop a finite element computational simulation model to predict the results of transcatheter edge-to-edge repair (TEER) interventions. METHODS AND RESULTS: We prospectively enrolled patients with secondary mitral regurgitation (MR) referred for a clinically indicated TEER. The 3D trans-oesophageal echocardiograms performed at the beginning of the procedure were used to perform the simulation. On the 3D dynamic model of the MV that was first obtained, we simulated the clip implantation using the same clip type, size, number, and implantation location that was used during the intervention. The 3D model of the MV obtained after the simulation of the clip implantation was compared with the clinical results obtained at the end of the intervention. We analysed the degree and location of residual MR and the shape and area of the diastolic MV area. We performed computational simulation on five patients. Overall, the simulated models predicted well the degree and location of the residual regurgitant orifice(s) but tended to underestimate the diastolic mitral orifice area. CONCLUSION: In this proof-of-concept study, we present preliminary results on our algorithm simulating clip implantation in five patients with functional MR. We show promising results regarding the feasibility and accuracy in terms of predicting residual MR and the need to improve the estimation of the diastolic MV area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.017 |
| 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".