Evidence map›Paper›PMID 32490403›Full record

ArticleProceedings. IEEE Conference on Virtual Reality and 3D User Interfaces2020

A Physics-based Virtual Reality Simulation Framework for Neonatal Endotracheal Intubation.

Xiao Xiao, Shang Zhao, Yan Meng, Lamia Soghier, Xiaoke Zhang, James Hahn

Abstract read
In one paragraph

Article in Proceedings. IEEE Conference on Virtual Reality and 3D User Interfaces, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation.International Symposium on Mixed and Augmented Reality : (ISMAR) [proceedings]. IEEE and ACM International Symposium on Mixed and Augmented Reality · 2020
    Article
  3. Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020
    Article
  4. Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiao XiaoGeorge Washington University.
Shang ZhaoGeorge Washington University.
Yan MengGeorge Washington University.
Lamia SoghierNational Children's Health Systems.
Xiaoke ZhangGeorge Washington University.
James HahnGeorge Washington University.

Funding

Neonatal Endotracheal Intubation: Enhancing Training Through Computer Simulation and Automated EvaluationR01HD091179 · NICHD · GEORGE WASHINGTON UNIVERSITY · PI HAHN, JAMES K, SOGHIER, LAMIA · 2017 to 2021
$1.6M
NICHD NIH HHS R01 HD091179
6 · The paper itself

Abstract

Neonatal endotracheal intubation (ETI) is a complex procedure. Low intubation success rates for pediatric residents indicate the current training regimen is inadequate for achieving positive patient out-comes. Computer-based training systems in this field have been limited due to the complex nature of simulating in real-time, the anatomical structures, soft tissue deformations and frequent tool interactions with large forces which occur during actual patient intubation. This paper addresses the issues of neonatal ETI training in an attempt to bridge the gap left by traditional training methods. We propose a fully interactive physics-based virtual reality (VR) simulation framework for neonatal ETI that converts the training of this medical procedure to a completely immersive virtual environment where both visual and physical realism were achieved. Our system embeds independent dynamics models and interaction devices in separate modules while allowing them to interact with each other within the same environment, which offers a flexible solution for multi-modal medical simulation scenarios. The virtual model was extracted from CT scans of a neonatal patient, which provides realistic anatomical structures and was parameterized to allow variations in a range of features that affect the level of difficulty. Moreover, with this manikin-free VR system, we can capture and visualize an even larger set of performance parameters in relation to the internal geometric change of the virtual model for real-time guidance and post-trial assessment. Lastly, validation study results from a group of neonatologists are presented demonstrating that VR is a promising platform to train medical professionals effectively for this procedure.

Indexed as

AnimationComputer graphicsComputing methodologiesGraphics systems and interfacesHaptic devicesHuman-centered computingHuman computer interaction (HCI)Interaction devicesModeling and simulationPhysical simulationReal-time simulationSimulation types and techniquesVirtual reality

Identifiers

PMID32490403
PMCPMC7266144

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.