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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".