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Deep Learning-Based Automatic Segmentation and Analysis of Mitochondrial Damage by Zika Virus and SARS-CoV-2
Brianda Alexia Agundis-Tinajero1,2, Miguel Ángel Coronado-Ipiña1,2, Ignacio Lara-Hernández2
1Department of Sciences, Autonomous University of San Luis Potosí (UASLP), San Luis Potosí 78295, Mexico.
Viruses
|September 27, 2025
Summary
A new deep learning tool automatically analyzes mitochondrial changes in virus-infected cells, accurately predicting immune responses. This AI approach speeds up research and aids in understanding cellular responses to pathogens like SARS-CoV-2.
Area of Science:
- Cell Biology
- Virology
- Artificial Intelligence
Background:
- Viral infections induce mitochondrial morphological changes linked to immune responses.
- Analyzing these changes offers insights into cellular immunity but is labor-intensive and prone to bias.
Purpose of the Study:
- To develop a deep learning (DL) approach for automated mitochondrial identification, segmentation, and analysis in virus-infected cells.
- To compare DL-based analysis with manual methods for accuracy and efficiency.
Main Methods:
- Utilized a U-Net convolutional neural network architecture for image analysis.
- Applied the DL model to transmission electron microscopy images of cells infected with SARS-CoV-2 variants and Zika virus.
- Performed comparative analysis of manual versus automated segmentation and morphological metrics.
Main Results:
- Achieved over 85% accuracy in automated mitochondrial segmentation and analysis.
- Demonstrated no statistically significant differences between manual and automated metrics.
- Significantly reduced processing time compared to manual analysis.
Conclusions:
- The DL-based tool accurately and efficiently analyzes mitochondrial ultrastructural changes in viral infections.
- This approach aids in predicting immune responses by detecting intact and damaged mitochondria.
- The tool has potential applications in studying emerging pathogens and automating cell biology research.
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