Evidence map›Paper›PMID 33564287›Full record

ArticlePersonal and ubiquitous computing2023

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19.

Dandan Zhang, Xiaoya Liu, Mingyue Shao, Yaping Sun, Qingyuan Lian, Hongmei Zhang

RetractedOpen access · bronzeAbstract readRetracted Publication
In one paragraph

Article in Personal and ubiquitous computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Dandan ZhangDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Xiaoya LiuDepartment of Cerebrovascular Surgery, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Mingyue ShaoDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Yaping SunDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Qingyuan LianDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Hongmei ZhangDepartment of Nursing, Henan Provincial People's Hospital, Zhengzhou, 450003 Henan China.
Henan Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

Indexed as

Artificial intelligenceComputed tomographyCOVID-19Imaging diagnosis

Identifiers

PMID33564287
PMCPMC7861001
OpenAlexW3128503969

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.