Dynamic Patient-Specific Three-Dimensional Simulation of Mitral Repair
Bibliographic record
Abstract
OBJECTIVE: Planned mitral repair strategies are generally established from preoperative echocardiography; however, specific details of the repair are often determined intraoperatively. We propose that three-dimensional printed, patient-specific, dynamic mitral valve models may help surgeons plan and trial all the details of a specific patient's mitral repair preoperatively. METHODS: Using preoperative echocardiography, segmentation, modeling software, and three-dimensional printing, we created dynamic, high-fidelity, patient-specific mitral valve models including the subvalvular apparatus. We assessed the accuracy of 10 patient mitral valve models anatomically and functionally in a heart phantom simulator, both objectively by blinded echocardiographic assessment, and subjectively by two mitral repair experts. After this, we attempted model mitral repair and compared the outcomes with postoperative echocardiography. RESULTS: Model measurements were accurate when compared with patients on anterior-posterior diameter, circumference, and anterior leaflet length; however, less accurate on posterior leaflet length. On subjective assessment, Likert scores were high at 3.8 ± 0.4 and 3.4 ± 0.7, suggesting good fidelity of the dynamic model echocardiogram and functional model in the phantom to the preoperative three-dimensional echocardiogram, respectively. Mitral repair was successful in all 10 models with significant reduction in mitral insufficiency. In two models, mitral repair was performed twice, using two different surgical techniques to assess which provided a better outcome. When compared with the actual patient mitral repair outcome, the repaired models compared favorably. CONCLUSIONS: Complex mitral valve modeling seems to predict an individual patient's mitral anatomy well, before surgery. Further investigation is required to determine whether deliberate preoperative practice can improve mitral repair outcomes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".