Fabrication of Nasoalveolar Molding Devices for the Treatment of Cleft Lip and Palate, Using Stereolithography Additive Manufacturing Processes and Computer-Aided Design Manipulation Software
Bibliographic record
Abstract
INTRODUCTION: Nasoalveolar molding (NAM) is a presurgical orthopedic treatment modality that attempts to reorient misaligned bony and soft tissue structures in patients with clefting of the lip and palate. The NAM devices are implemented prior to surgical intervention in order to minimize the gap across the cleft and thereby reduce tension across the eventual repair. Currently, NAM devices are fabricated in a laboratory and then refined chairside by the provider. The present article describes the potential of three-dimensional (3D) printing and computer-aided design (CAD) software for the fabrication of NAM devices. MATERIAL AND METHODS: A workflow was developed to demonstrate the use of 3D printing and CAD software to design NAM devices. This workflow encompasses scanning an impression into CAD software, performing a series of manipulations, and then printing the digital model. RESULTS: To test the workflow, a cleft palate plaster model was scanned into CAD software. Through a series of linear and angular freeform manipulations of the body, the model was modified to display a cleft with a reduced alveolar gap. Sequential molding devices were produced which would gradually apply pressure to targeted areas of hard and soft tissue until the cleft is minimized. The resulting devices are printed using a stereolithography printer. CONCLUSIONS: The use of 3D printing and CAD software shows promise in improving the accuracy, speed, and cost-effectiveness of designing NAM devices. The accuracy and flexibility from digitally visualizing the manipulations made to an appliance before its creation can result in a more personalized device for the patient.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".