A Semi-Automatic Method to Create an Affordable Three-Dimensional Printed Splint Using Open-Source and Free Software
Bibliographic record
Abstract
Three-dimensional (3D) printed splints are becoming more feasible in recent years, showing promising lightweight, waterproof, and hygienic designs. A typical procedure to create 3D printed splints is obtaining the geometry of a body segment using a 3D scanner, creating a 3D printable splint model based on the geometry of the body segment, and 3D printing the splint. As technologies of 3D scanning and 3D printing become mature gradually, the main challenge to fabricate 3D printed splint is to create 3D printable splint models. To solve this challenge, researchers have proposed various methods to design 3D splint models. However, most methods require extensive 3D modeling skills that medical professionals are lacking. In this work, a semi-automatic method is proposed to create a 3D printable model. Given the geometry of a body segment obtained through a 3D scanner, the method includes three key steps: (1) create a draft splint lattice structure, (2) optimize the splint structure, and (3) create a 3D printable model based on the optimized structure. All the software adopted for this method is free and readily available, and thus, there is no additional cost to convert from a scanned geometry of a body segment to a 3D printable splint model. Because the majority of the work is done automatically, with initial training, a medical professional should be able to create a 3D printable model using the proposed method, given the geometry of a body segment. The proposed method is demonstrated by creating a 3D printed wrist splint and the demo is uploaded into GitHub, a popular open-source platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".