Computational Tools for Patient-specific Surgical Planning of Tetralogy of Fallot Repair
Bibliographic record
Abstract
Surgical repair of the congenital heart defect Tetralogy of Fallot (TOF) involves a series of steps to remove right ventricular outflow tract (RVOT) and pulmonary artery (PA) obstruction. However, there is a large degree of geometric variability among TOF anatomies, and the most suitable repair strategy for a given patient is unknown preoperatively. The goal of this thesis is to further our understanding of the relationships between geometry and hemodynamics in the TOF patient population, as well as the patient-specific factors that may influence the choice of surgical repair strategy, using computational methods including computational fluid dynamics (CFD) simulations and virtual surgery techniques. Four studies make up this thesis: the first study presents a methodology to evaluate geometric parameters of the RVOT and PAs and applies this methodology to 11 postoperative TOF patients. Results showed significant variability within the dataset. The second study conducts a morphometric and statistical shape analysis of 16 postoperative TOF patients, as well as computational fluid dynamics (CFD) simulations on the patient-specific geometries. Results demonstrated a negative relationship between cumulative and maximum torsion and the energy efficiency of the geometry, highlighting the importance of avoiding the introduction of torsion during surgical repair. The third study virtually induces a range of stenoses on three TOF geometries to investigate the sensitivity of the efficiency of different anatomies to geometric modification. Results found differences in the peak efficiency across the patients, motivating the pursuit of patient-specific surgical repair strategies. Finally, the fourth study applies virtual surgery techniques to a preoperative TOF anatomy, to explore the effect of different surgical repair strategies on postoperative hemodynamics and efficiency. Results revealed significant differences in the postoperative pressure gradients and energy efficiency for the range of repair strategies, and enabled selection of the optimal repair technique for the patient. Overall, this thesis underscores the utility and benefit of computational tools for patient-specific, surgical planning of TOF repair. Including computational tools in this process has the potential to enable identification of the patient’s optimal surgical repair strategy preoperatively, reducing guesswork in the operating room and improving long-term outcomes for this patient population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".