Evidence map›Paper›PMID 34355163›Full record

ArticleFrontiers in artificial intelligence2021

AI-Assisted CT as a Clinical and Research Tool for COVID-19.

Zion Tsz Ho Tse, Sierra Hovet, Hongliang Ren, Tristan Barrett, Sheng Xu, Baris Turkbey, Bradford J Wood

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

7 authors.

Zion Tsz Ho TseDepartment of Electronic Engineering, The University of York, York, United Kingdom.
Sierra HovetDepartment of Electronic Engineering, The University of York, York, United Kingdom.
Hongliang RenDepartment of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Tristan BarrettDepartment of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
Sheng XuCenter for Interventional Oncology, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States.
Baris TurkbeyCenter for Interventional Oncology, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States.
Bradford J WoodCenter for Interventional Oncology, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States.

Funding

Atlanta Clinical and Translational Science Institute (ACTSI) RenewalUL1TR000454 · NCATS · EMORY UNIVERSITY · PI STEPHENS, DAVID S · 2012 to 2016
$25.8M
Development of COVID-19 Imaging Tools with Artificial IntelligenceZIABC011936 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2020 to 2025
$1.2M
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Artificial Intelligence with Chest Imaging in COVID-19 and Isolation and Ventilator Devices for COVID-19ZIACL090072 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2020 to 2025
$0k
NCATS NIH HHS UL1 TR000454
6 · The paper itself

Abstract

There is compelling support for widening the role of computed tomography (CT) for COVID-19 in clinical and research scenarios. Reverse transcription polymerase chain reaction (RT-PCR) testing, the gold standard for COVID-19 diagnosis, has two potential weaknesses: the delay in obtaining results and the possibility of RT-PCR test kits running out when demand spikes or being unavailable altogether. This perspective article discusses the potential use of CT in conjunction with RT-PCR in hospitals lacking sufficient access to RT-PCR test kits. The precedent for this approach is discussed based on the use of CT for COVID-19 diagnosis and screening in the United Kingdom and China. The hurdles and challenges are presented, which need addressing prior to realization of the potential roles for CT artificial intelligence (AI). The potential roles include a more accurate clinical classification, characterization for research roles and mechanisms, and informing clinical trial response criteria as a surrogate for clinical outcomes.

Indexed as

artificial intelligencecomputed tomographyCOVID-19diagnosisRT-PCR

Identifiers

PMID34355163
PMCPMC8329033

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.