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Updated: Sep 28, 2025
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
Published on: January 27, 2023
[Research progress of micronucleus visualization analysis and artificial intelligence detection strategy]
1School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China School of Medical Science and Engineering, Beihang University, Beijing 100191, China.
Abstract:
The micronucleomics test can comprehensively display a variety of harmful endpoints, such as DNA damage and repair, chromosome breakage or loss and cell growth inhibition, with fast, simple and economical feature. Micronucleomics is not only widely used in the comprehensive assessment of the types and modes of genetic action of exogenous chemicals (such as drugs, food additives, cosmetics, environmental pollutants, etc.), but also plays an important role in the screening and risk assessment of cancer population at high risk. However, the traditional micronucleomics image counting method has the characteristics of time-consuming, low accuracy, and high cost, which cannot meet the current analysis requirements of large-scale, multi-index, rapidity, high precision and visualization. In recent years, with the rapid development of the era of precision medicine based on big data, visualized analysis of new micronucleomics based on machine learning and detection strategies based on deep learning have shown a good application prospect. This review, based on the application value of micronucleomics, systematically compares the traditional and new artificial intelligence counting of micronucleus images, and discusses the future direction of micronucleus image detection.
Insights
The micronucleomics test assesses genetic damage, but traditional methods are slow and costly. New AI-powered image analysis offers a faster, more accurate approach for genetic toxicology and cancer risk assessment.
Area of Science:
- Genomics
- Toxicology
- Biotechnology
Background:
- Micronucleomics assays detect genetic damage like DNA breaks and chromosome loss.
- These assays are vital for evaluating chemical toxicity and cancer risk.
- Traditional manual counting methods are inefficient, inaccurate, and expensive.
Purpose of the Study:
- To review the application value of micronucleomics.
- To compare traditional manual counting with new AI-driven image analysis.
- To discuss future directions in micronucleus image detection.
Main Methods:
- Systematic comparison of traditional and AI-based micronucleus image counting.
- Review of machine learning and deep learning strategies for visualized analysis.
- Analysis of micronucleomics applications in genetic toxicology and cancer risk assessment.
Main Results:
- Traditional micronucleomics counting is time-consuming, inaccurate, and costly.
- AI-based methods offer rapid, precise, and visualized analysis of micronucleus images.
- New AI approaches align with precision medicine and big data requirements.
Conclusions:
- AI-powered micronucleomics significantly improves efficiency and accuracy over traditional methods.
- Machine learning and deep learning are crucial for advancing micronucleomics in toxicology and risk assessment.
- Future research should focus on further developing and validating AI detection strategies for broader application.
