Evidence map›Paper›PMID 34447599›Full record

ArticleIntelligent medicine2021

Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.

Yadong Gang, Xiongfeng Chen, Hanlun Wang, Jianying Li, Ying Guo, Bin Wen, Jinxiang Hu, Haibo Xu, Xinghuan Wang

Open access · goldAbstract read
In one paragraph

Article in Intelligent medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 31% 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, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
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.

Yadong GangDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Xiongfeng ChenDepartment of Radiology, Puren Hospital affiliated to Wuhan University of Science and Technology, Wuhan 430080, China.
Hanlun WangDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Jianying LiGE Healthcare, Computed Tomography Research Center, Beijing 100176, China.
Ying GuoGE Healthcare, Computed Tomography Research Center, Beijing 100176, China.
Bin WenTAMI Intelligence Technology (Beijing) Co. Ltd, Beijing 100176, China.
Jinxiang HuDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Haibo XuDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Xinghuan WangDepartment of Urology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
Wuhan University · CNWuhan University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging .

methodsFrom February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups.

resultsCompared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s

conclusionsThe automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

Indexed as

Artificial intelligenceComputer tomographyCoronavirus disease 2019Robotics

Identifiers

PMID34447599
PMCPMC8156837
OpenAlexW3165230347

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.