Evidence map›Paper›PMID 32038861›Full record

ArticleHealthcare technology letters2019

Virtual linear measurement system for accurate quantification of medical images.

Gavin Wheeler, Shujie Deng, Kuberan Pushparajah, Julia A Schnabel, John M Simpson, Alberto Gomez

Abstract read
In one paragraph

Article in Healthcare technology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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.

Gavin WheelerSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Shujie DengSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Kuberan PushparajahSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Julia A SchnabelSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
John M SimpsonSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Alberto GomezSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.ORCID 0000-0002-7897-7589

Funding

Department of Health II-LA-0716-20001Wellcome Trust 203148/Z/16/Z
6 · The paper itself

Abstract

Virtual reality (VR) has the potential to aid in the understanding of complex volumetric medical images, by providing an immersive and intuitive experience accessible to both experts and non-imaging specialists. A key feature of any clinical image analysis tool is measurement of clinically relevant anatomical structures. However, this feature has been largely neglected in VR applications. The authors propose a Unity-based system to carry out linear measurements on three-dimensional (3D), purposefully designed for the measurement of 3D echocardiographic images. The proposed system is compared to commercially available, widely used image analysis packages that feature both 2D (multi-planar reconstruction) and 3D (volume rendering) measurement tools. The results indicate that the proposed system provides statistically equivalent measurements compared to the reference 2D system, while being more accurate than the commercial 3D system.

Indexed as

3D echocardiographic images3D measurement tools3D volume rendering measurement toolsclinical image analysis toolechocardiographyimage analysis packagesimage reconstructionlinear measurementsmedical image processingrendering (computer graphics)Unity-based systemvirtual linear measurement systemvirtual realityvolumetric medical imagesVR applications

Identifiers

PMID32038861
PMCPMC6952242

What OpenQuestion holds

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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.