Evidence map›Paper›PMID 38459073›Full record

ArticleScientific reports2024

Clinical electromagnetic brain scanner.

Amin Abbosh, Konstanty Bialkowski, Lei Guo, Ahmed Al-Saffar, Ali Zamani, Adnan Trakic, Aida Brankovic, Alina Bialkowski, Guohun Zhu, David Cook and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

11 citing papers in PubMed, 24 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Amin AbboshSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia. a.abbosh@uq.edu.au.
Konstanty BialkowskiSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Lei GuoSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Ahmed Al-SaffarSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Ali ZamaniSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Adnan TrakicSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Aida BrankovicSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Alina BialkowskiSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
Guohun ZhuSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
David CookFaculty of Medicine, The University of Queensland, St Lucia, QLD4072, Australia.
Stuart CrozierSchool of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD4072, Australia.
University of Queensland · AU

Funding

Australian Department of Industry, Innovation and Science, Cooperative Research Centres Projects (CRC-P) Grants CRC-P60941
6 · The paper itself

Abstract

Stroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.

Indexed as

BrainStrokeElectromagnetic PhenomenaHeadHumansMagnetic Resonance ImagingTomography, X-Ray Computed

Identifiers

PMID38459073
PMCPMC10923816
OpenAlexW4392583414

What OpenQuestion holds

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LicenceCC BY
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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.