ArticleJMIR XR and spatial computing2024
Virtual Reality in Clinical Teaching and Diagnostics for Liver Surgery: Prospective Cohort Study.
Article in JMIR XR and spatial computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Achieving Gameplay Independence in Virtual Reality Exergames for Individuals With Mild Intellectual Disabilities: Pilot Study.JMIR serious games · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Learning and applying anatomy are essential but are studied and done through 2D tools and imaging techniques. This study aims to verify the usefulness of an additional 3D technique and ensure an improvement in the visualization of anatomical structures and pathological findings. Objective: The study aims to examine the usefulness of virtual reality (VR) technology as an additional tool in medical diagnostics. Groups of students, residents, and specialists in surgery, radiology, and internal medicine evaluated magnetic resonance imaging (MRI) by answering a multiple-choice questionnaire. Subsequently, a virtual 3D display was used for processing. The questionnaire focused on topographical conditions and the transfer of academic knowledge into clinical application. The main objective was to determine anatomical understanding in a comparison between sectional image (2D) presentation and additional VR (3D) presentation, measured through correctly answered questions and processing time. The system usability scale (SUS) was integrated as another criterion for VR usability. Methods: The cross-over study assessed 63 participants regarding their knowledge of liver anatomy and pathologies based on an interindividual comparison. Group formation according to the respective level of medical training was as follows: students (n=35), residents (n=15), and specialists (n=13). Participants answered 25 multiple-choice questions first using sectional imaging (MRI) in a 2D environment (computer screen) and afterward with the respective segmented 3D model visualized in a VR simulation. The main criteria for the analyses were the number of correctly answered questions and processing time. A customized SUS was used to analyze VR usability. Missing data analysis showed that there were no accounted missing data. Results: The rate of correct answers improved significantly with the additional use of VR ( Conclusions: The additional use of VR suggests statistically significant improvements across all groups. VR seems to enable students and residents to participate in diagnostics and create treatment plans at an early stage. Transferred to clinical practice, this may lead to improvement in diagnostics and interventions. The lack of randomization and a potential learning effect are the main limitations to be addressed in future studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.