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

Perspective of smart nanocapsule swallowable laser-guided for integrated sensing and crispr-mediated cancer gene editing.

Cancer gene therapy·2026
Same author

Improving the measurement resolution in BOTDR sensor with optimized wavelet denoising strategy.

PloS one·2026
Same author

Optical Spectroscopy of Cerebral Blood Flow for Tissue Interrogation in Ischemic Stroke Diagnosis.

ACS chemical neuroscience·2025
Same author

Needle-Free Targeted Injections Using Bubble Laser Technology in Therapeutics.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis.

ACS synthetic biology·2024
Same author

Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities.

Mikrochimica acta·2024

Related Experiment Video

Updated: Aug 22, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM

Bakr Ahmed Taha1, Yousif Al Mashhadany2, Abdulmajeed H J Al-Jumaily3

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Viruses
|November 11, 2022
PubMed
Summary

This study introduces a novel recurrent neural network (RNN) model to analyze SARS-CoV-2 (the virus that causes COVID-19) morphometry from transmission electron microscopy (TEM) images, enabling accurate virus level prediction.

Keywords:
RNNSARS-CoV-2artificial intelligencemorphometrytransmission electron microscopy

More Related Videos

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

845
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Aug 22, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
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

845
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Virology
  • Medical Imaging
  • Computational Biology

Background:

  • The rapid spread of SARS-CoV-2 necessitates deeper understanding of its cell biology and ultrastructural characteristics.
  • Current research on SARS-CoV-2 ultrastructure is limited, hindering comprehensive analysis.
  • Accurate viral characterization is crucial for developing effective diagnostics and treatments.

Purpose of the Study:

  • To analyze the viral morphometry of SARS-CoV-2 using transmission electron microscopy (TEM) images.
  • To develop and validate a prediction model for identifying SARS-CoV-2 levels based on morphometric features.
  • To establish a fully automated system for high-accuracy virus diagnosis and research.

Main Methods:

  • Image analysis of SARS-CoV-2 morphometry, including width, height, circularity, roundness, aspect ratio, and solidity.
  • Optimization of a recurrent neural network (RNN) for predicting virus levels from TEM images.
  • Development of a fully automated system for virus diagnosis and analysis.

Main Results:

  • The RNN model achieved exceptional performance with a low error score (3.216 × 10-11) and high accuracy in predicting virus levels.
  • Morphometric features such as size and shape were identified as key indicators for virus level determination.
  • The automated system demonstrated a high ability to predict virus levels, indicating its potential for practical application.

Conclusions:

  • The developed RNN model and automated system offer a powerful tool for SARS-CoV-2 research and diagnostics.
  • Accurate morphometric analysis can significantly aid in virus diagnosis, mutation prevention, and understanding the viral life cycle.
  • This approach advances medical virology by providing a high-accuracy method for virus characterization and drug development.