Limited access to museum and prosection models: how 3D scanning and 3D printing can help
Bibliographic record
Abstract
Background A large number of universities are dependent upon prosections for their anatomy laboratory. Prosections are time‐demanding to maintain and to produce. Technological advancement in the 21 st century now offers tremendous new resources for teaching/learning anatomy: virtual reality, multimedia, 3D models, plastinations, etc. In spite of the high quality of the multimedia‐based modalities, it is suggested that physical 3D models are still superior and more appropriate for learning anatomy when students are tested on 3D material, and are more representative of their future practice. Commercially available 3D models, such as those printed from segmented cadaveric images, or plastinated models are extremely expensive. Recent technological advancement has made high‐resolution 3D scanning and 3D printing more affordable. Structured‐light scanning is a technique for accurately creating 3D surface models by projecting a known pattern of light onto the object, then capturing and analyzing the distortion of the pattern using a camera system. It has been used and validated for soft tissue morphology recording for clinical and research purposes. The reported 3D resolution of this surface scanner is 0.1 mm, with a 3D point accuracy of up to 0.05 mm. The aim of this communication is to present an affordable technique to produce 3D printed replicates of prosections and museum models. This initiative can provide students the opportunity to learn from the restricted fragile models in an effort to preserve them and to reduce the handling by students. METHODS Anatomic material, prosection specimens, and museum models were first scanned using an Artec 3D scanner (Artec Spider; Palo Alto, CA), and the digital replicate was refined using the associated software. If needed, the digital model was modified using the commercially available CAD software, Materialise Magics (Materialise, Leuven, BE) to further design it before 3D printing. Using the 3D printer Ultimaker 3 Extended and associated software (Geldermalsen, Netherlands), the digital model was then printed with PLA (polylactic acid) and PVA (polyvinyl alcohol); the PVA acts as the dissolvable support material, and the PLA as the final product. Once printed, models were made to look realistic through collaboration with a medical illustrator. Discussion Complex structures have been reproduced with success using this method (Fig. 1). Students can handle these models to obtain the haptic experience, while observing the demonstration done on the original prosection from which the prints were made. This reduces the handling of delicate prosections which can affect their integrity. This is also a good alternative to reduce the prosection dependence of prosection‐based laboratories; replicates can be reproduced at a high accuracy, at minimal printing cost, and infinitely. The 3D scanner and 3D printer were acquired for the price of a few commercially available 3D printed models or of one medium size plastination specimen. CONCLUSION This low‐cost technique allows the production of 3D replicates of complex prosections and museum models that can be safely handled by a large number of students in and outside of the laboratory setting. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".