Evidence map›Paper›PMID 42897548›Full record

ReviewRespiratory research2026

Electrical impedance tomography in neonatal respiratory diseases: a clinical review of applications and evidence.

Yifan Wang, Yaping Song, Caiju Xing, Feng Fu, Xun Jiang, Yan Lin

Abstract readReview
In one paragraph

Review in Respiratory research, 2026. 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

6 authors.

Yifan Wang *Department of Pediatrics, Tangdu Hospital, Air Force Medical University, Fourth Military Medical University, Xin-Si Street #569, Xi'an, 710038, China.
Yaping Song *Department of Pediatrics, Tangdu Hospital, Air Force Medical University, Fourth Military Medical University, Xin-Si Street #569, Xi'an, 710038, China.
Caiju XingDepartment of Pediatrics, Tangdu Hospital, Air Force Medical University, Fourth Military Medical University, Xin-Si Street #569, Xi'an, 710038, China.
Feng FuBiomedical Engineering, Air Force Medical University, Fourth Military Medical University, Chang-Le Street #169, Xi'an, 710038, China.
Xun JiangDepartment of Pediatrics, Tangdu Hospital, Air Force Medical University, Fourth Military Medical University, Xin-Si Street #569, Xi'an, 710038, China.
Yan LinDepartment of Pediatrics, Tangdu Hospital, Air Force Medical University, Fourth Military Medical University, Xin-Si Street #569, Xi'an, 710038, China. 43961190@qq.com.ORCID https://orcid.org/0000-0002-0061-071X

Funding

Air Force Medical University General Clinical Research Project 2022LC2232Air Force Medical University Second Affiliated Hospital Independent Innovation Project 2023CTDQN019Air Force Medical University Second Affiliated Hospital Interdisciplinary Integration Project 2024JCRH021National Natural Science Foundation of China 82000522National Natural Science Foundation of China 82270563Xi'an Science and Technology Plan Project 24YXYJ0122
6 · The paper itself

Abstract

Neonatal respiratory diseases remain a leading cause of morbidity and mortality in intensive care settings, with management complicated by the limitations of current monitoring tools. Chest radiography provides only static snapshots and cannot capture the dynamic evolution of lung aeration, while lung ultrasound, though portable, remains operator-dependent and incapable of quantifying regional ventilation. Electrical Impedance Tomography (EIT) has emerged as a noninvasive, radiation free imaging modality that addresses these gaps by enabling real time, continuous visualization of regional lung function at the bedside. This review synthesises evidence from studies published between 2011 and 2026, examining how EIT informs the management of neonatal respiratory distress syndrome, acute respiratory distress syndrome, bronchopulmonary dysplasia, pneumothorax, and the transitions inherent to mechanical ventilation. Across these disease contexts, EIT reveals regional ventilation heterogeneity invisible to conventional monitors, provides immediate physiological feedback during therapeutic interventions, and enables early detection of complications before clinical deterioration. We identify key barriers to clinical adoption, including technical limitations, lack of standardised protocols, and the need for outcome driven trials, and propose a research agenda to accelerate translation. By reframing EIT not merely as an imaging tool but as a dynamic decision support system integrated into clinical workflows, we aim to provide clinicians with a framework for leveraging this technology to improve outcomes in the most vulnerable patients.

Indexed as

Electric ImpedanceRespiratory Tract DiseasesTomographyHumansInfant, NewbornRespiratory Distress Syndrome, NewbornElectrical impedance tomographyNeonatePulmonary impedanceRespiratory diseases

Identifiers

PMID42897548
PMCPMC13681881

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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.