AI and flow cytometry.

Journal: Journal of immunology (Baltimore, Md. : 1950)
Published Date: Feb 9, 2026

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming biotechnology and playing a key role in bioeconomy. One of the most important measurement capabilities at the forefront of biotechnology innovations is flow cytometry (FCM), a high-throughput, single-cell analysis platform technology. However, the quality and consistency of FCM data can vary significantly across laboratories and study datasets, resulting in millions of FCM datasets siloed for their use in AI applications. This workshop focuses on overcoming challenges and identifying solutions that include essential measurements, reference controls, AI-ready reference data, and current AI/ML models. It aims to advance AI/ML applications in FCM and related data.

Authors

  • Dawei Lin
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Anupama Gururaj
    Division of Allergy, Immunology, and Transplantation, NIAID, NIH, Rockville, MD, USA.
  • Sheng Lin-Gibson
    Biosystems and Biomaterials Division, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, United States.
  • Lili Wang
    School of Logistics, Chengdu University of Information Technology, Chengdu, China.

Keywords

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