Evidence map›Paper›PMID 36675075›Full record

ArticleInternational journal of molecular sciences2023

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T

Adrian Truszkiewicz, Dorota Bartusik-Aebisher, Łukasz Wojtas, Grzegorz Cieślar, Aleksandra Kawczyk-Krupka, David Aebisher

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 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
–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

1 citing paper in PubMed.

  1. Article
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.

Adrian TruszkiewiczDepartment of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, Poland.ORCID 0000-0002-5762-0492
Dorota Bartusik-AebisherDepartment of Biochemistry and General Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Kopisto 2a Street, 35-959 Rzeszów, Poland.ORCID 0000-0002-5557-5464
Łukasz WojtasDepartment of Medical Equipment, Provincial Clinical Hospital No. 2, 35-959 Rzeszów, Poland.
Grzegorz CieślarDepartment of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland.ORCID 0000-0003-2210-8744
Aleksandra Kawczyk-KrupkaDepartment of Internal Medicine, Angiology and Physical Medicine, Center for Laser Diagnostics and Therapy, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland.
David AebisherDepartment of Photomedicine and Physical Chemistry, Medical College of University of Rzeszów, University of Rzeszów, Warzywna 1A Street, 35-310 Rzeszów, Poland.ORCID 0000-0002-2661-6570

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.

Indexed as

Artificial IntelligenceNeoplasmsAlgorithmsCell Culture TechniquesHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNeural Networks, ComputerMATLABMRrelaxation timesT1T2

Identifiers

PMID36675075
PMCPMC9861169

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.