Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2022 Aug 26

PMID 36016071

Authors

Ilona Karpiel

Ana Starcevic

Mirella Urzeniczok

Affiliations

Soon will be listed here.

Abstract

The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.

Citing Articles

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PMID: 38075874 PMC: 10706942. DOI: 10.3389/fmicb.2023.1320856.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Batra S, Sharma H, Boulila W, Arya V, Srivastava P, Khan M Sensors (Basel). 2022; 22(19).

PMID: 36236573 PMC: 9571822. DOI: 10.3390/s22197474.

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