Evaluating the integration of body donor imaging into anatomical dissection using augmented reality
Notice bibliographique
Résumé
Recent advancements in anatomy education have incorporated the use of augmented reality (AR) into medical curricula. AR has begun to emerge as a particularly useful tool since students can overlay diagnostic imaging (ex: MRI, CT scans) directly onto an anatomical specimen or model. Studies evaluating the use of the Microsoft HoloLens, a brand of AR smart glasses, in anatomical education have suggested the benefits of this tool mainly for self‐study while also describing its overall use as difficult and pointing out the necessity for technical support. Many of these studies, however, did not use objective measures to assess the modality’s implementation and/or use. The purpose of this investigation to analyze the effects of the AR modality on student learning and cadaveric dissection experience into a fourth‐year dissection‐based medical course offered at McGill University. A convergent parallel mixed methods approach was used comprising of both quantitative and qualitative data collection phases. Students registered in the course were separated into two groups, one group receiving diagnostic imaging to view on a HoloLens device and the other group on an iPad. Student responses to a study participant questionnaire and anatomical mental rotation test (AMRT), assessing spatial ability, were evaluated quantitatively. Qualitative data included written transcripts from focus group interviews conducted with both study groups following the course. Survey results were analyzed and compared across study groups using non‐parametric statistics; an unpaired, Mann Whitney U test. AMRT data was evaluated using parametric statistical analyses; one‐way ANOVA with Sidak’s post‐hoc test. Qualitative data was analyzed using inductive and deductive coding, followed by thematically organizing student responses from focus group interviews into relevant themes. IRB# A12‐E82‐17B. Overall, students in the HoloLens group expressed difficulty using the HoloLens to project body donor imaging and understanding the interaction between the projected imaging with their dissection, in comparison to their iPad group counterparts:. These findings were all statistically significant. Additionally, AMRT data showed no statistically significant differences between groups, both pre‐ and post‐AFS. HoloLens students were also more inclined to agree their imaging modality motivated their learning. In the focus group interviews, students also shared that the incorporation of radiology in AFS and the HoloLens device had a positive impact on their anatomy education. The HoloLens AR device in this investigation was able to increase student motivation, promote appreciation of the imaging overlay and provide an enhanced dissection experience. For these reasons, this investigation shows promise that AR can successfully be used in anatomical medical curricula. Support or Funding Information The authors would like to thank the support provided by the Dr. Clarke K. McLeod Memorial Scholarship (to KM) and Class of Medicine 1974 Faculty Scholar for Teaching Excellence & Innovation in Medical Education as well as The Centre for Medical Education Innovation and Research Seed Fund (to GPJCN).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».