Evidence map›Paper›PMID 37638201›Full record

ArticleFrontiers in neurology2023

Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities.

Jieshi Ma, Jie Guo, Yang Li, Zheng Wang, Yunpeng Dong, Jianxing Ma, Yan Zhu, Guan Wu, Liang Yi, Xuetao Shi

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 26% 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, 7 citations in OpenAlex.

  1. 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

10 authors at 3 institutions in 1 country.

Jieshi MaDepartment of Medical Engineering, Army Medical Center of PLA, Chongqing, China.
Jie GuoInstitute of Medical Research, Northwestern Polytechnical University, Xi'an, China.
Yang LiDepartment of Medical Engineering, Army Medical Center of PLA, Chongqing, China.
Zheng WangDepartment of Neurosurgery, Army Medical Center of PLA, Chongqing, China.
Yunpeng DongDepartment of Neurosurgery, Army Medical Center of PLA, Chongqing, China.
Jianxing MaDepartment of Neurosurgery, Army Medical Center of PLA, Chongqing, China.
Yan ZhuHangzhou Utron Technology Co., Ltd., Hangzhou, China.
Guan WuHangzhou Utron Technology Co., Ltd., Hangzhou, China.
Liang YiDepartment of Neurosurgery, Army Medical Center of PLA, Chongqing, China.
Xuetao ShiDepartment of Medical Electronic Engineering, School of Biomedical Engineering, Air Force Medical University of PLA, Xi'an, China.
Army Medical University · CNAir Force Medical University · CNNorthwestern Polytechnical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The purpose of this paper is to compare the differences in the features of multifrequency electrical impedance tomography (MFEIT) images of human heads between healthy subjects and patients with brain diseases and to explore the possibility of applying MFEIT to intracranial abnormality detection. Methods: Sixteen healthy volunteers and 8 patients with brain diseases were recruited as subjects, and the cerebral MFEIT data of 9 frequencies in the range of 21 kHz - 100 kHz of all subjects were acquired with an MFEIT system. MFEIT image sequences were obtained according to certain imaging algorithms, and the area ratio of the ROI (AR_ROI) and the mean value of the reconstructed resistivity change of the ROI (MVRRC_ROI) on both the left and right sides of these images were extracted. The geometric asymmetry index (GAI) and intensity asymmetry index (IAI) were further proposed to characterize the symmetry of MFEIT images based on the extracted indices and to statistically compare and analyze the differences between the two groups of subjects on MFEIT images. Results: There were no significant differences in either the AR_ROI or the MVRRC_ROI between the two sides of the brains of healthy volunteers ( Conclusion: There were significant differences in the geometric symmetry and the signal intensity symmetry of the reconstructed targets in the MFEIT images between healthy volunteers and patients with brain diseases, and the above findings provide a reference for the rapid detection of intracranial abnormalities using MFEIT images and may provide a basis for further exploration of MFEIT for the detection of brain diseases.

Indexed as

area and signal intensity of ROIcerebraldetectionimage featuresintracranial abnormalityMFEITsymmetry

Identifiers

PMID37638201
PMCPMC10457004
OpenAlexW4385755458

What OpenQuestion holds

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LicenceCC BY
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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.