Advanced multimedia applications for teaching anatomy: a comparison of software used to generate 3D anatomical models
Bibliographic record
Abstract
Reform in medical education has resulted in decreasing anatomy instructional hours and increasing emphasis on medical imaging and clinical application. Recent trends necessitate exploration of alternative teaching methods that augment traditional lecture and cadaveric dissection experiences. Currently, there are a variety of advanced multimedia applications emerging as potential teaching tools. The aim of this study is to compare and contrast the development of 3D anatomical models using two different segmentation and rendering software programs: Amira and OsiriX. Models of the upper limb will be assessed on ease of creation and applications within learning environments. With Amira (v.4.1), accurate reconstructions of anatomical structures are generated from cryosection images obtained from the Visible Human Project. Both manual and semi‐automatic techniques are used to highlight and segment structures of interest. Through stereoscopic projection, the highly detailed model may enhance understanding of complex spatial relationships in lecture or laboratory settings. In contrast, OsiriX is a free program that provides an intuitive interface for managing and viewing cross‐sectional CT and MRI data. It is capable of quickly constructing 3D models through an automatic process. Subsequently, models can be integrated into presentation software. Grant Funding Source Internal
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".