Prediction of postoperative 3D spine shape using Controlled Point Deformation Network (CPDNet)
Bibliographic record
Abstract
3D pointset deformation controlled by shape properties is still a challenging task in shape modelling. Existing neural networks learn point-wise feature vectors, then predict point displacements to deform 3D shapes. However, these solutions often learn features independently between points, i.e. without considering neighborhood constraints. In this paper, we propose a deep learning architecture named Controlled Point Deformation Network (CPDNet), which exploits shape properties to predict a postoperative spine shape as the outcome of corrective surgery to treat scoliosis. CPDNet learns the rigid transformations between the 3D landmarks of consecutive vertebrae in the spine. Point-wise feature vectors are extracted from the 3D preoperative spine and concatenated with patients’ selected clinical metadata using a fully convolutional network. Then, 3D point-wise displacement vectors are predicted and added to the input points to obtain the postoperative spine shape. Geometric shape loss computes the differences between the 3D geometric coordinates of the predicted and target spine shapes. Rigid transformation loss computes the differences in rotation and translation between consecutive vertebrae, leading the network to learn spinal shape properties. We trained and validated our model on 99 patients who previously underwent posterior spinal fusion surgery. On the test set, our model achieves average errors of 1.5°, 7.6°, and 4.9° for three clinical indices, namely coronal balance and Cobb angles in the sagittal and coronal planes, respectively, outperforming the state-of-the-art P2P-NET model. Our model could serve to develop a surgical planning tool for scoliosis treatment, allowing surgeons and patients to visualize the predicted result of spinal surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".