Evidence map›Paper›PMID 34840591›Full record

ArticleBiomedical signal processing and control2022

Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation.

Adel Oulefki, Sos Agaian, Thaweesak Trongtirakul, Samir Benbelkacem, Djamel Aouam, Nadia Zenati-Henda, Mohamed-Lamine Abdelli

Open access · greenAbstract read
In one paragraph

Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 25 citations in OpenAlex.

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  8. COVIDiagnostics (Basel, Switzerland) · 2022
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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

7 authors at 3 institutions in 3 countries.

Adel OulefkiCentre de Développement des Technologies Avancées (CDTA), PO. Box 17 Baba Hassen, Algiers 16081, Algeria.
Sos AgaianDept. of Computer Science, College of Staten Island, New York, 2800 Victory Blvd Staten Island, New York 10314, USA.
Thaweesak TrongtirakulFaculty of Industrial Education, Rajamangala University of Technology Phra Nakhon, 399 Samsen Rd. Vachira Phayaban, Dusit, Bangkok 10300, Thailand.
Samir BenbelkacemCentre de Développement des Technologies Avancées (CDTA), PO. Box 17 Baba Hassen, Algiers 16081, Algeria.
Djamel AouamCentre de Développement des Technologies Avancées (CDTA), PO. Box 17 Baba Hassen, Algiers 16081, Algeria.
Nadia Zenati-HendaCentre de Développement des Technologies Avancées (CDTA), PO. Box 17 Baba Hassen, Algiers 16081, Algeria.
Mohamed-Lamine AbdelliEL-BAYANE, Center for Radiology and Medical Imaging, Bordj Bou Arréridj 34000, Algeria.
Centre de Développement des Technologies Avancées · DZCollege of Staten Island · USRajamangala University of Technology Phra Nakhon · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease (COVID-19) is a severe infectious disease that causes respiratory illness and has had devastating medical and economic consequences globally. Therefore, early, and precise diagnosis is critical to control disease progression and management. Compared to the very popular RT-PCR (reverse-transcription polymerase chain reaction) method, chest CT imaging is a more consistent, sensible, and fast approach for identifying and managing infected COVID-19 patients, specifically in the epidemic area. CT images use computational methods to combine 2D X-ray images and transform them into 3D images. One major drawback of CT scans in diagnosing COVID-19 is creating false-negative effects, especially early infection. This article aims to combine novel CT imaging tools and Virtual Reality (VR) technology and generate an automatize system for accurately screening COVID-19 disease and navigating 3D visualizations of medical scenes. The key benefits of this system are a) it offers stereoscopic depth perception, b) give better insights and comprehension into the overall imaging data, c) it allows doctors to visualize the 3D models, manipulate them, study the inside 3D data, and do several kinds of measurements, and finally d) it has the capacity of real-time interactivity and accurately visualizes dynamic 3D volumetric data. The tool provides novel visualizations for medical practitioners to identify and analyze the change in the shape of COVID-19 infectious. The second objective of this work is to generate, the first time, the CT African patient COVID-19 scan datasets containing 224 patients positive for an infection and 70 regular patients CT-scan images. Computer simulations demonstrate that the proposed method's effectiveness comparing with state-of-the-art baselines methods. The results have also been evaluated with medical professionals. The developed system could be used for medical education professional training and a telehealth VR platform.

Indexed as

COrona-VIrus Disease (COVID-19)COVID lesion segmentationLesion measurementsVR visualization

Identifiers

PMID34840591
PMCPMC8610934
OpenAlexW3215193005

What OpenQuestion holds

Textmetadata
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.