Design of an interactive brain model for neuroanatomy education and <scp>MRI</scp> training
Bibliographic record
Abstract
In this article, we introduce a new virtual application that offers an interactive model of the brain for neuroanatomy education. Through a dual-platform architecture, the application can be downloaded on both desktop and mobile devices, with the mobile app leveraging unique capacities of modern handheld systems to deploy the brain model in augmented reality. In addition to illustrating complex spatial relationships between internal brain structures, vasculature, and cranial nerves, the application integrates magnetic resonance imaging (MRI) data into the user interface. MRI series in the coronal, sagittal, and axial planes can be superimposed directly onto the brain model, allowing students to engage with two-dimensional MRI slices in three-dimensional space. While previous virtual tools have offered a similar superimposition, none have done so through a mobile app, downloadable on handheld devices and suited to the modern student. The benefits of this function on students' spatial understanding and identification of neural structures on MRI slices remain understudied. The aim of this article is to describe the functionality of our dual-platform application, to outline its potential strengths as an educational tool, and to address possible directions for improvement following future assessments of the app's utility. Our ultimate goal is to offer a preliminary introduction to a new system that seeks to support users' understanding of three-dimensional neuroanatomy and aims to enhance their ability to read an MRI of the brain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.002 |
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".