EP09.03: Transposition of great arteries: three‐dimensional virtual and physical models from obstetrical ultrasound data
Bibliographic record
Abstract
A diabetic 34-year-old woman, gravida 2 para 1, was referred for fetal echocardiography evaluation. During the fetal echocardiogram (25 weeks of gestation), abnormal outflow tracts showing a parallel course of the great arteries raised a strong suspicion of simple transposition of great arteries (TGA). The diagnosis was subsequently confirmed by the anatomical characteristics of the arteries that arose from each ventricle. Fetal echocardiogram was performed using a 3D high-resolution probe (4–8-MHz transducer, Voluson E9, GE Healthcare, Zipf, Austria). For post-processing images, software 3D Slicer (Birmington, UK) and Elucis (Realize Medical, Ottawa, ON, Canada) enabled the correct segmentation and texture improvement of the 3D US image. Using the software Elucis, we could segment the fetal heart in a virtual reality (VR) after import the images obtained through 4D-STIC and insert them into the application platform. A 3D model of the fetal heart was printed using a resin 3D printer (J5 MediJet 3D Printer Stratasys, USA). The 3D physical and virtual models were used in this case as interesting additional diagnostic tools to the current standard imaging armamentarium, enabling us to improve the quality of prenatal parental counselling and to optimise of cardiac surgical planning. The male neonate was born by Caesarean section and the surgical correction of TGA (arterial switch) was performed. Currently, he is clinically well, and has a mild aortic insufficiency. Acknowledgement: Financial support from FAPERJ Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".