Clinical Applications of Virtual and Augmented Reality in Radiology: A Scoping Review
Notice bibliographique
Résumé
Background: Virtual reality (VR) and augmented reality (AR) have emerged as innovative tools in healthcare, particularly using diagnostic and interventional imaging methods, offering new avenues for enhancing patient care and procedural outcomes. Their applications range from improving preoperative planning and pain management to providing advanced procedural support and training. Despite their growing integration into clinical practice, evidence of their cost-effectiveness and specific clinical benefits when using radiological tools remains limited. This review aims to map the current landscape of VR and AR applications using radiological modalities and highlight areas for future research. Objective: This scoping review explores the clinical applications of VR and AR in different radiological fields, aiming at assessing target areas, cost-effectiveness, and benefits of these technologies. Methods: We conducted a comprehensive literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A total of 15 primary studies were included, covering diverse populations and applications of VR and AR. Results: In total, 15 studies (N = 781 patients) were included, with sample sizes ranging from 6 to 120. These studies highlighted various clinical applications of VR and AR, including imaging-guided preoperative planning, pain management, and procedural support. Although several studies demonstrated improvements in patient experiences and diagnostic accuracy, cost-effectiveness data were lacking. Notably, 47% of the studies focused exclusively on pediatric populations (N = 363), and 33% were randomized controlled trials. Quality assessment using the STARD criteria revealed that 60% of studies were rated as good (score > 12), 27% as fair (score 10–12), and 13% as suboptimal (score < 10), with inter-reader reliability showing substantial agreement (ICC = 0.76; 95% CI: 0.64–0.91). Out of 15 included studies, only 6 (40%) reported statistically significant improvements in patient experiences, with the remaining studies reporting positive trends (e.g., feasibility, usability, improved planning). Individual studies demonstrated significant benefits of VR interventions; for instance, one study reported a reduction in distress scores by a mean of 3.0 (95% CI: 1.0–5.0) and a decreased need for parental presence (risk ratio 0.3; 95% CI: 0.1–0.7; p < 0.001) compared to conventional methods. Conclusions: VR and AR technologies hold promise in enhancing patient care and procedural outcomes. Future research should focus on the cost-effectiveness of these technologies and identify specific target populations that would benefit the most. Additionally, adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines should be encouraged to ensure transparent and comprehensive reporting in VR and AR studies.
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 | 0,014 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,021 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».