Evidence map›Paper›PMID 35387221›Full record

ArticleComputational and mathematical methods in medicine2022

Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome.

Yuhan Wu, Sheng Zhao, Xiaohong Yang, Chunxue Yang, Zhen Shi, Qin Liu, Yubo Wang, Meilan Qin, Li Zhang

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. 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 It has been retracted, and should not be counted.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 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. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Yuhan WuDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-5065-6783
Sheng ZhaoDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-5810-7842
Xiaohong YangDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0003-2017-0237
Chunxue YangDepartment of Ultrasound, Caidian District People's Hospital of Wuhan, Hubei Province 430100, China.ORCID https://orcid.org/0000-0002-8103-6392
Zhen ShiDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0001-6532-0994
Qin LiuDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0001-6480-4639
Yubo WangDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-9820-089X
Meilan QinDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-1346-634X
Li ZhangDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.ORCID https://orcid.org/0000-0002-2005-5354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to analyze the application of ultrasonic lung imaging diagnosis model based on artificial intelligence algorithm in neonatal respiratory distress syndrome (NRDS), an ultrasonic lung imaging diagnosis model based on a deep residual network (DRN) was proposed. In this study, 90 premature infants in the hospital were selected as the research object and divided into the experimental group (45 cases) and control group (45 cases) according to whether or not they have NRDS. DRN was compared with the deep residual network (DRWSR) based on wavelet domain, deep residual network detection with normalization framework (Fisher-DRN), and distorted image edge detection preprocessor (DIEDP). Then, it was applied to the diagnosis of NRDS. The clinical data and ultrasound imaging results of infants with NRDS and ordinary premature infants were compared. The results showed that the gestational age, birth weight, and Apgar scores of the NRDS group were remarkably lower than those of ordinary children (

Indexed as

Artificial IntelligenceRespiratory Distress Syndrome, NewbornAlgorithmsChildHumansInfantInfant, NewbornInfant, PrematureLungUltrasonography

Identifiers

PMID35387221
PMCPMC8977311

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Registered trials

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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.