Evidence map›Paper›PMID 35634067›Full record

ArticleComputational intelligence and neuroscience2022

Intelligence Classification Algorithm-Based Drug-Resistant Pulmonary Tuberculosis Computed Tomography Imaging Features and Influencing Factors.

Yanping Jiang, Xinguo Zhao, Zhengfei Fan

Open access · hybridAbstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 46% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Yanping JiangDepartment of Tuberculosis, Wuxi No.5 People's Hospital, Wuxi 214000, Jiangsu, China.ORCID https://orcid.org/0000-0001-6371-3890
Xinguo ZhaoDepartment of Tuberculosis, Wuxi No.5 People's Hospital, Wuxi 214000, Jiangsu, China.ORCID https://orcid.org/0000-0001-9260-989X
Zhengfei FanDepartment of Emergency, Huzhou Central Hospital, Huzhou 313000, Zhejiang, China.ORCID https://orcid.org/0000-0002-8354-4577
Wuxi No.2 People's Hospital · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The drug resistance and influencing factors of patients with pulmonary tuberculosis were investigated, and a dual attention dilated residual network (DADRN) algorithm was proposed. The algorithm was applied to process and analyze lung computed tomography (CT) images of 400 included patients with pulmonary tuberculosis. Besides, sparse code book algorithm and bag of visual word (BOVW) algorithms were introduced and compared, and the influencing factors of pulmonary tuberculosis drug resistance were analyzed. The results demonstrated that the localization precision of lung consolidation, nodules, and cavities by the DADRN algorithm reached 91.2%, 92.5%, and 93.8%, respectively. The recall rate of the three algorithms amounted to 83.55%, 84.5%, and 86.4%, respectively. Both localization precision and recall rate of the DADRN algorithm were higher than those of other two algorithms (

Indexed as

TuberculosisTuberculosis, Multidrug-ResistantTuberculosis, PulmonaryAdultAlgorithmsAntitubercular AgentsHumansIntelligenceIsoniazidLungMiddle AgedRifampinStreptomycinTomography, X-Ray ComputedAntitubercular AgentsIsoniazidRifampinStreptomycin

Identifiers

PMID35634067
PMCPMC9135543
OpenAlexW4280544247

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.