Cranial Remodeling Orthosis for Infantile Plagiocephaly Created Through a 3D Scan, Topological Optimization, and 3D Printing Process
Bibliographic record
Abstract
ABSTRACT Purpose This article presents a novel design of a cranial remodeling orthosis (CRO) helmet developed through a three-dimensional (3D) scanning and 3D printing process to correct an infantile plagiocephaly. Materials and Methods This research merges a handheld scanner, computer-aided engineering (CAE), and fused deposition modeling (FDM) technologies to propose an alternative to traditional plaster casting. The study finds out all criteria that will merge with requirements, 3D scanning, topological optimization into the CAE, and 3D printing to implement the design for manufacturing (DFM) approach to get a reproducible process that is less invasive for the child. Results The project identifies the current limitations and creates design requirements and acceptance criteria to define a design and manufacturing process using a topological optimization method. Based on a child's skull 3D scan, the application aims to manufacture a CRO helmet due to clinical criteria by 3D printing. The new design aims to reduce the time from assessment to initial fitting and to reduce the temperature within the CRO. The project has manufactured a CRO helmet by fused depositing modeling in 3D printing to characterize its mechanical behavior and analyze the possible improvements. Conclusions Limitations were found in the material used in the 3D printing, and some recommendations are made to improve the method. The DFM approach is useful for improving the final product by considering manufacturing and use constraints as soon as possible in the design stage, such as part orientation, infill density, and topological optimization parameters as well as the practitioner' skills. The main novelty is to have developed a 3D scanning and 3D printing process to correct an infantile plagiocephaly to obtain a CRO helmet responding to use and manufacturing constraints while proposing a suitable organic shape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".