3D Neuroanatomy: Using the HoloLens for an augmented reality approach in neuroanatomy education
Bibliographic record
Abstract
The gold standard for neuroanatomy instruction involves the use of human brain specimens and prosections; however, there is still a clear need for innovative visualizations and educational tools that supplement dissection and improve learning outcomes for students in many areas including health related disciplines. With technological advances in computer imaging and display devices, anatomy educators increasingly have a variety of options for delivering educational content. There are limitations to these approaches, which include challenges in accurately representing 3D structures, ease of use for instructors and students, as well as oversimplification or inaccurate information. Students consistently struggle with integrating 2D images into a complex representation of the interconnected brain, and overlaying this understanding with important vascular and clinically relevant information. Increasingly, 3D technologies are being employed to help close this gap in student learning; however, not all 3D technologies are alike, nor do they offer the same instructional value. The goal of the current project was to develop an augmented reality (AR) teaching tool that could be implemented in neuroanatomy instruction, and to evaluate its effectiveness in the classroom. In collaboration with Microsoft (BigPark Vancouver and Microsoft Garage Interns), an interactive lecture was developed using the HoloLens, a mixed reality headset offering the unique ability to blend virtual 3D content with the real world. Specifically, 3D reconstructions of basal ganglia nuclei were obtained from MRI scans and built into the teaching tool deployed on the HoloLens. Taking advantage of the HoloLens gesture recognition input system, interactivity was optimized, allowing the 3D reconstructions to be manipulated with ease and built up separately, as well as to visualize 2D MRI scans juxtaposed against 3D volumes of key brain nuclei. We are evaluating the pedagogical efficacy of this HoloLens teaching tool for undergraduate students, compared to traditional instructional methods. Assessments comprised multiple‐choice questions and structure identification on brain specimens focused on spatial orientation within the CNS as well as the ability to orient a 2D slice to the 3D structure of the brain. Students in both groups received baseline testing to assess both previous content knowledge as well as visuospatial reasoning to control for individual differences. Support or Funding Information PJH, TSB supported by Mitacs Accelerate Internships in partnership with Microsoft. Equipment donated from Microsoft. MG, RC supported by Emerging Media Lab, UBC Studios. This abstract is from the Experimental Biology 2018 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".