Logo-ajmb
Submitted: 13 Sep 2020
Accepted: 09 Nov 2020
ePublished: 30 Dec 2020
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - Firefox Plugin)

Avicenna J Med Biochem. 2020;8(2): 120-127.
doi: 10.34172/ajmb.2020.17
  Abstract View: 2427
  PDF Download: 641

Review Article

A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

Mohammad Hosein Sadeghi 1 ORCID logo, Hamid Omidi 1 ORCID logo, Sedigheh Sina 1,2* ORCID logo

1 Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
2 Radiation Research Center, School of Mechanical Engineering, Shiraz University, Iran.
*Corresponding Author: *Corresponding author: Sedigheh Sina, Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz, Iran. Tel: +989172228349, Fax: +987116473474, Email: , Email: samirasina@shirazu. ac.ir

Abstract

Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed.

Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies.

Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively.

Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.

First Name
Last Name
Email Address
Comments
Security code


Abstract View: 2425

Your browser does not support the canvas element.


PDF Download: 641

Your browser does not support the canvas element.