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Virtual Reality, Autostereoscopy, and Physical Models for Learning Anatomy: Performance Comparisons

2022· article· en· W4225426882 on OpenAlexaff
Simran Lohit, Isabella R. Reis, Rowan Ives, Sachi Chan, Evangelea Touliopoulos, Sakshi Sinha, Amit Nehru, Veronica DeYoung, Josh Mitchell, Danielle Brewer‐Deluce, Ranil Sonnadara, Bruce Wainman

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsCompute CanadaWestern UniversityMcMaster University
Fundersnot available
KeywordsVirtual realityComputer scienceStereoscopyAutostereoscopyModalitiesAnatomyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Introduction Traditional anatomical education relies on textbooks, physical models, and cadaveric specimens. Advances in technology have provided other display modalities including virtual reality (VR), and autostereoscopic displays. VR uses head‐mounted displays (HMDs) for an interactive experience and autostereoscopic screens, like Alioscopy TM , offer a stereoscopic but HMD‐free environment. Current research shows contradictory results on the efficacy of VR compared to physical models, and there appears to be no data on the efficacy of autostereoscopic displays in learning anatomy. The purpose of this study is to determine whether VR, Alioscopy TM , or physical models yield the best performance for anatomical education. Methods Students at McMaster University without prior anatomy training will learn nominal skeletal anatomy in three different modalities: VR (Oculus Quest 2 TM ), Alioscopy TM , or a physical 3D‐printed bone model. Each of the environments will be as identical as possible (i.e, the VR environment is a rendering of the exact room and set‐up used for testing the physical bone models). Participants will be randomized to one of three interventions where they will study ten bony landmarks on either the human hemipelvis, zygomatic bone, or calcaneus in a distinct modality. Participants will be seated and use an Xbox TM controller to rotate the bone along the vertical axis of rotation. After four minutes, an untimed, recognition‐based test will be administered, where participants will be given a 3D‐printed bone identical to the one used in the learning phase, with randomized landmarks and word bank of landmarks learnt. Performance will be evaluated based on landmarks correctly identified on the recognition‐based test. Results Based on the current literature, we hypothesize that the physical models will be a superior learning environment compared to the VR environment. However, significant improvements in the HMD of the Oculus Quest 2 may have ameliorated previous issues with the VR environment. There is no data available on the efficacy of Alioscopy TM and while the absence of an HMD is appealing, its narrow viewing angle compared to the immersive VR environment may be problematic. Conclusion With the push to increase accessibility and decrease costs associated with anatomical education with the help of digital modalities, the findings from this study are critical to informing teaching practices, and technology use in anatomy education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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