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Updated: Jun 24, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Artificial Intelligence in Hematology.

Renjith Verghese¹, Ashif Iqubal², Kundan Mishra³

¹Department of Clinical hematology and Stem Cell Transplant, Command Hospital (SC), Pune, India.

Indian Journal of Hematology & Blood Transfusion : an Official Journal of Indian Society of Hematology and Blood Transfusion

|June 23, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) significantly enhances hematology diagnostics, particularly in image analysis and data interpretation for conditions like myelodysplastic syndromes. Advanced deep learning models show high accuracy, promising future improvements in overcoming current diagnostic limitations.

Keywords:

AI Artificial intelligence Clinical hematology Deep learning Hematology Machine learning

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Last Updated: Jun 24, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

Hematology
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) is increasingly vital for healthcare diagnosis and management.
Effective AI deployment relies on robust software for medical image analysis and deep learning algorithms.
AI has notably advanced hematology in cellular, molecular, and genetic analysis over the past decade.

Purpose of the Study:

To review the impact and applications of AI in hematology diagnostics.
To highlight AI's role in analyzing medical images and electronic health records.
To discuss the potential of emerging AI models and omics data in addressing current diagnostic challenges.

Main Methods:

Review of AI applications in hematology, focusing on deep learning for image classification and interpretation.
Analysis of AI performance metrics, including accuracy and precision-recall rates for specific hematological tasks.
Examination of AI integration with multimodal diagnostic workflows and omics data.

Main Results:

AI achieves over 95% accuracy in classifying peripheral blood smears.
AI demonstrates a precision-recall rate exceeding 94% for cellular-level feature detection and segmentation.
Multimodal AI workflows report over 95% accuracy in differentiating myelodysplastic syndrome subtypes.

Conclusions:

AI, particularly deep learning, has made substantial progress in hematology diagnostics.
Despite AI's success, traditional methods like microscopy and flow cytometry analysis remain crucial but have interpretation limitations.
Future advancements in AI, including foundation models, transformers, generative diffusion models, and omics data integration, hold promise for overcoming existing diagnostic hurdles.