Evidence map›Paper›PMID 35495888›Full record

ArticleComputational and mathematical methods in medicine2022

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration.

Junfeng Sun, Xiaojun Zheng, Qiang Gao, Xiaofeng Wang, Yu Qiao, Jialong Li

RetractedOpen access · hybridAbstract 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 It has been retracted, and should not be counted.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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 44% 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, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Junfeng SunDepartment of Neurosurgery, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.
Xiaojun ZhengDepartment of Neurology, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.
Qiang GaoDepartment of Neurosurgery, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.
Xiaofeng WangDepartment of Neurosurgery, Third Hospital of Baoji City, Baoji, 721000 Shaanxi, China.
Yu QiaoDepartment of Neurosurgery, Third Hospital of Baoji City, Baoji, 721000 Shaanxi, China.
Jialong LiDepartment of Neurosurgery, Third Hospital of Baoji City, Baoji, 721000 Shaanxi, China.ORCID https://orcid.org/0000-0002-2969-5761
Baoji City Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two patients with intracerebral hemorrhage were divided into experimental group (46 cases, minimally invasive aspiration therapy) and control group (46 cases, traditional craniotomy therapy) according to different treatment methods, and CT image scanning was performed. In addition, a CT image segmentation model of intracerebral hemorrhage based on improved fuzzy C-means clustering algorithm (n-FCM) was proposed to process the CT images of the patients. The results showed that the Dice coefficient of n-FCM algorithm after the addition of salt and pepper noise was 0.89, which was higher than that of traditional algorithm; the average operation time of experimental group was 58.93 ± 5.33 min, which was significantly lower than that of control group (90.21 ± 16.24 min) (

Indexed as

Artificial IntelligenceStrokeAlgorithmsCerebral HemorrhageHumansTomography, X-Ray ComputedUnited States

Identifiers

PMID35495888
PMCPMC9054454
OpenAlexW4285685380

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.