Evidence map›Paper›PMID 39065936›Full record

ReviewSensors (Basel, Switzerland)2024

Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring.

Ziqiang Cui, Xinyan Liu, Hantao Qu, Huaxiang Wang

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
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

4 authors.

Ziqiang CuiSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Xinyan LiuSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Hantao QuSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Huaxiang WangSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.ORCID 0000-0002-8792-0760

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.

Indexed as

Electric ImpedanceLungTomographyAlgorithmsCOVID-19HumansImage Processing, Computer-AssistedLung DiseasesMonitoring, Physiologicelectrical impedance tomographyimage reconstructioninstrumentationpulmonary monitoringventilation/perfusion

Identifiers

PMID39065936
PMCPMC11281055

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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.