Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Digital twins in pulmonary medicine: a scoping review of applications, benefits, and challenges.

BMC pulmonary medicine·2026
Same author

Development and validation of a minimum data set for total joint arthroplasty registries.

BMC musculoskeletal disorders·2026
Same author

Machine learning-based prediction of bronchiolitis in children under two years: a multicenter study within a single metropolitan area.

BMC infectious diseases·2026
Same author

Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.

Health science reports·2026
Same author

Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A Retrospective Study on Clinical Data.

Health science reports·2026
Same author

Predicting mortality risk of COVID-19 among chronic kidney disease patients using machine learning algorithms.

Health informatics journal·2026

Related Experiment Video

Updated: Sep 30, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

921

Developing an artificial neural network for detecting COVID-19 disease.

Mostafa Shanbehzadeh1, Raoof Nopour2, Hadi Kazemi-Arpanahi3,4

  • 1Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.

Journal of Education and Health Promotion
|March 14, 2022
PubMed
Summary

This study developed an artificial neural network (ANN) model for accurate COVID-19 diagnosis. The ANN model achieved high accuracy, aiding early detection and intervention for coronavirus disease 2019.

Keywords:
Artificial intelligentCOVID-19coronavirusdecision support systemsmachine learningneural network

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.3K

Related Experiment Videos

Last Updated: Sep 30, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

921
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.3K

Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Biology
  • Infectious Disease Modeling

Background:

  • Coronavirus disease 2019 (COVID-19) has caused a global health crisis.
  • Accurate and timely diagnosis is crucial for effective patient management.
  • Clinical decision-making for COVID-19 presents significant challenges.

Purpose of the Study:

  • To develop an intelligent diagnostic model for COVID-19 using artificial neural networks (ANNs).
  • To enhance early detection and intervention strategies for COVID-19 patients.

Main Methods:

  • Utilized a dataset of 250 confirmed COVID-19 and 150 negative cases.
  • Employed correlation coefficient technique for feature selection (P < 0.05).
  • Applied back-propagation for training and evaluated various ANN configurations.

Main Results:

  • Identified 18 significant predictor variables for the ANN model.
  • The optimal ANN configuration (9-10-15-2 layers) achieved an Area Under the Curve (AUC) of 0.982.
  • Demonstrated high diagnostic performance: 96.4% sensitivity, 90.6% specificity, and 94% accuracy.

Conclusions:

  • The proposed ANN-based system offers a robust computational tool for COVID-19 diagnosis.
  • This clinical decision support system can aid frontline practitioners in early detection.
  • Potential to improve patient outcomes and reduce mortality rates associated with COVID-19.