Three-dimensional Construction of Tissue Casting Molds for Aortic Arch Reconstruction in Hypoplastic Left Heart Syndrome
Bibliographic record
Abstract
All articles of this category Objectives: Aortic arch reconstruction is a key step of the Norwood operation in hypoplastic left heart syndrome (HLHS). Inadequate geometry leads to significant morbidity. We assessed aortic geometry and growth after arch reconstruction and developed a computational model to generate tissue casting molds. Methods: A computational model was developed based on anatomic dimensions derived by echocardiography. Software engineering was performed with Matlab R2015b (Mathworks, Natick, MA, United States). Mimics (Materialise, Leuven, Belgium) was used for three-dimensional reconstruction of CT data. CAD postprocessing was performed with SketchUp Make (Trimble Navigation Ltd., Sunnyvale, CA, United States). Results: The algorithm included diameters of the hypoplastic ascending aorta, main pulmonary artery and descending aorta. Furthermore, geometry was defined by height of the native aortic arch and distance between pulmonary artery and descending aorta. To respect the spatial relations of the ascending aorta, the pulmonary artery and the descending aorta, the model incorporated the angle between the ascending and descending aorta with regard to the pulmonary artery. Further adaptations had to be performed to account for the larger proximal neoaortic dimensions due to the Damus-Kay-Stansel anastomosis. Alignment of the non-parallel orientation of the pulmonary artery to descending aorta axis and the ascending to descending aorta axis was achieved by tilting of the construct. Export functions to the STereoLithography (STL) file format were added to enable export to CAD software, postprocessing and printing of sterilizable models. Virtual reconstruction was performed after data export. The tissue scaffold was virtually fused with the native anatomy of a patient with HLHS to evaluate adequacy of the theoretical approach and the computer algorithm. Conclusion: We developed a software tool that is close to its clinical application. The parameters used can be acquired easily in patients with HLHS and do not necessitate complex diagnostic procedures. Three-dimensional printing offers the possibility to sterilize the tissue scaffold and to use it as an individualized, readily available casting mold for customized patches.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".