AI and flow cytometry

Area of Science:

• Biotechnology
• Computational Biology
• Data Science

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly vital in biotechnology and the bioeconomy.
• Flow cytometry (FCM) is a critical high-throughput single-cell analysis technology for biotechnology innovation.
• Significant variations in FCM data quality and consistency across studies create data silos, limiting AI applications.

Purpose of the Study:

• To address challenges in flow cytometry (FCM) data quality and consistency for AI applications.
• To identify solutions for creating AI-ready reference data in FCM.
• To foster advancements in AI/ML applications for FCM data analysis.

Main Methods:

• Focus on essential measurements for standardized FCM data.
• Development of reference controls to ensure data consistency.
• Exploration of current AI/ML models applicable to FCM data.
• Establishing AI-ready reference datasets for FCM.

Main Results:

• Identified key challenges in FCM data quality and consistency.
• Proposed solutions including essential measurements and reference controls.
• Highlighted the need for AI-ready reference data.
• Reviewed current AI/ML models for FCM data analysis.

Conclusions:

• Standardized, high-quality FCM data is essential for effective AI/ML implementation.
• Developing AI-ready reference datasets will accelerate AI applications in FCM.
• Collaboration and standardized approaches are crucial for advancing AI in biotechnology through FCM.