Accuracy of medical models made by consumer-grade fused deposition modelling printers
Bibliographic record
Abstract
Background Additive manufacturing using fused deposition modelling (FDM) has become widely available with the development of consumer-grade three-dimensional printers. To be useful in maxillofacial surgery, models created by these printers must accurately reproduce the craniofacial skeleton. Objective To determine the accuracy of consumer-grade FDM printers in the production of medical models compared with industrial selective laser sintering (SLS) printers. Methods Computed tomography images of a dry skull were manipulated using OsiriX (OsiriX, Switzerland) and ZBrush (Pixologic, USA) software. Models were fabricated using a consumer-grade FDM printer at 100 μm, 250 μm and 500 μm layer heights and an industrial SLS printer. Seven linear measurements were made on the models and compared with the corresponding dry skull measurements using an electronic caliper. Results A dimensional error of 0.30% was observed for the SLS models and 0.44%, 0.52% and 1.1% for the 100 μm, 250 μm and 500 μm FDM models, respectively. Conclusion Consumer-grade FDM printers can produce medical models with sufficient dimensional accuracy for use in maxillofacial surgery. With this technology, surgeons can independently produce low-cost maxillofacial models in an office setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".