Scoping Review: The Use of Augmented Reality in Medical and Surgical Anatomical Education and Its Assessment Tools
Notice bibliographique
Résumé
Introduction With the increasing accessibility to new technologies such as virtual reality and augmented reality (AR), a growing number of educators are exploring how to incorporate such advances to their field of study; anatomical education is no exception. Aim The purpose of this study was to identify the different AR modalities used to teach anatomy to students, medical/veterinarian trainees and surgeons via coursework, and/or procedural training. We also examined the qualitative and quantitative assessment tools used to evaluate the performance of various AR technologies in specific teaching settings. Methods A scoping review of the Web of Science, Pubmed, Embase and Medline was performed. Search terms were variations of 1) augmented reality, 2) medical or anatomical teaching/education/training, and 3) anatomy or radiology or cadaver. Abstracts, full articles, conference presentations, and guidelines published between January 2000–September 12, 2018 were identified and screened as per Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Virtual reality was an exclusion criterion. The number of participants, level of training, name of AR modality, aim of the study, setting of the study (anatomy course vs. procedural training), object that was “augmented”, body system studied, type of assessment tools, and relevant findings were extracted from accepted studies. Results Preliminary findings suggests that Microsoft Hololens and projection‐based modalities using Microsoft Kinect were the most prevalent modalities. The studies were mainly conducted in the context of an anatomy course, which primarily assessed usability, learner satisfaction and perceived benefits of AR through questionnaires using variations of the Likert scale. Certain studies also incorporated more objective findings such as pre‐ and post‐AR knowledge tests. However, the majority of those studies failed to use validated tests. Discussion/Conclusion The current literature seems supportive of the use of AR as an adjunctive teaching tool at the very least. However, the evidence is weak given the lack of studies with robust quantitative and qualitative methodology to objectively determine the influence of the integration of AR on anatomical education. Sufficiently powered studies using validated assessment tools must be conducted to better understand the role of AR in anatomical education. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».