Evidence map›Paper›PMID 40288983›Full record

ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2025

[Application of electrical impedance tomography in diagnosis and monitoring of pulmonary diseases].

Xiaomin Hu, Shuaifu Zhang, Panfeng Chen, Feng Dong, Haojun Fan, Qi Lyu, Yanbin Xu

Abstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2025. 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
–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

0 citing papers in PubMed.

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

7 authors.

Xiaomin HuSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China.
Shuaifu ZhangSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China.
Panfeng ChenDepartment of Respiratory and Critical Care Medicine, Tianjin First Central Hospital, Tianjin 300192, P. R. China.
Feng DongSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China.
Haojun FanInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, P. R. China.
Qi LyuInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, P. R. China.
Yanbin XuSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrical impedance tomography (EIT) is a new non-invasive functional imaging technology, which has the advantages of non-invasion, non-radiation, low cost, fast response, portability and visualization. In recent years, more and more studies have shown that EIT has great potential in the detection of lung diseases and has been applied to early diagnosis and treatment of some diseases. This paper introduced the basic principle of EIT, discussed the research and clinical application of EIT in the detection of acute respiratory distress syndrome, chronic obstructive pulmonary disease, pneumothorax and pulmonary embolism, and focused on the summary and introduction of indicators and functional images of EIT related to the detection of lung diseases. This review will help medical workers understand and use EIT, and promote the further development of EIT in lung diseases as well as other fields.

Indexed as

Electric ImpedanceLung DiseasesTomographyHumansPulmonary Disease, Chronic ObstructivePulmonary EmbolismRespiratory Distress SyndromeAuxiliary diagnosisElectrical impedance tomographyPulmonary diseaseVentilation distribution

Identifiers

PMID40288983
PMCPMC12035636

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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.