Evidence map›Paper›PMID 30588506›Full record

ArticleTomography (Ann Arbor, Mich.)2018

Evaluation of Virtual Reality for Detection of Lung Nodules on Computed Tomography.

Brian J Nguyen, Aman Khurana, Brendon Bagley, Andrew Yen, Richard K J Brown, Jadranka Stojanovska, Michael Cline, Mitchell Goodsitt, Sebastian Obrzut

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 25% of its field
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

1 citing paper in PubMed, 11 citations in OpenAlex.

  1. 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

9 authors at 2 institutions in 1 country.

Brian J NguyenDepartment of Radiology, University of California San Diego, La Jolla, CA; and.
Aman KhuranaDepartment of Radiology, University of California San Diego, La Jolla, CA; and.
Brendon BagleyDepartment of Radiology, University of California San Diego, La Jolla, CA; and.
Andrew YenDepartment of Radiology, University of California San Diego, La Jolla, CA; and.
Richard K J BrownDepartment of Radiology, University of Michigan, Ann Arbor, MI.
Jadranka StojanovskaDepartment of Radiology, University of Michigan, Ann Arbor, MI.
Michael ClineDepartment of Radiology, University of Michigan, Ann Arbor, MI.
Mitchell GoodsittDepartment of Radiology, University of Michigan, Ann Arbor, MI.
Sebastian ObrzutDepartment of Radiology, University of California San Diego, La Jolla, CA; and.
University of California San Diego Medical Center · USUniversity of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual reality (VR) systems can offer benefits of improved ergonomics, but their resolution may currently be limited for the detection of small features. For detection of lung nodules, we compared the performance of VR versus standard picture archiving and communication system (PACS) monitor. Four radiologists and 1 novice radiologist reviewed axial computed tomography (CTs) of the thorax using standard PACS monitors (SM) and a VR system (HTC Vive, HTC). In this study, 3 radiologists evaluated axial lung-window CT images of a Lungman phantom. One radiologist and the novice radiologist reviewed axial lung-window patient CT thoracic images (32 patients). This HIPAA-compliant study was approved by the institutional review board. Detection of 227 lung nodules on patient CTs did not result in different sensitivity with SM compared with VR. Detection of 23 simulated Lungman phantom lung nodules on CT with SM resulted in statistically greater sensitivity (78.3%) than with VR (52.2%,

Indexed as

Computed tomographyCost savingLung noduleVirtual CTVirtual reality

Identifiers

PMID30588506
PMCPMC6299745
OpenAlexW2904160147

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.