Introduction to the Application of Machine Learning and Artificial Intelligence for Flow Cytometry

About the Speaker

Andrea Wang is the co-founder and CEO of AHEAD Medicine Corporation, pioneering AI solutions in clinical flow cytometry. Her academic foundation in translational biology and molecular medicine from Baylor College of Medicine, coupled with extensive pharmaceutical industry experience at GlaxoSmithKline, Mundipharma, and Bayer Healthcare, provides her with unique insights into healthcare innovation.

Since co-founding the AHEAD research team in 2017, Andrea has established strategic partnerships with leading institutions including UPMC, Roswell Park Comprehensive Cancer Center, Johns Hopkins Medicine, and BD Biosciences. Her team specializes in developing automated analysis solutions for complex flow cytometry data in hematological diseases. An International Innovator recognized by ISAC (2024), Andrea contributed to "Flow Cytometry Protocols, 5th Edition" and received the Taiwan AI Impact Award. She actively advances the field through her roles in the NIST Flow Cytometry Standards Consortium, co-leading CYTO Technology Showcase under ISAC's innovation committee, and developing the fcs4.0 data standard under ISAC’s data committee.

Webinar Summary

This webinar will provide an overview of artificial Intelligence (AI), machine learning (ML) and applications in flow cytometry data analysis. Participants will learn the foundational concepts of AI, understanding how ML functions as a subset of AI, and Deep Learning as a subset of ML. The session progresses from fundamental definitions to practical applications, covering key ML types including supervised, unsupervised, and reinforced learning approaches. Special emphasis is placed on the complete flow cytometry workflow, from data preprocessing and cleaning through cell classification and visualization. Real-world examples illustrate how these technologies enhance workflow efficiency and scalability in clinical flow cytometry laboratories. This overview is designed to equip attendees with practical knowledge for implementing AI solutions while maintaining high standards of flow cytometry data analysis.

Learning Objectives

1.Understand the definition and difference of AI and ML 

2.Understand how to evaluate AI model performance

3.Understand the type of AI and application examples

4.Understand future trends and challenges in the field

Who Should Attend

Clinical laboratory scientists and technologists, Flow cytometry specialists and operators, Clinical pathologists and hematologists, Biomedical researchers using flow cytometry, Laboratory directors and managers, Bioinformaticians and data analysts working with cytometry data, Medical technology students and residents, Healthcare professionals interested in AI applications in laboratory medicine.

CMLE Credit: 1.0