Abstract 21 Machine Learning Approach for Automatic Enumeration of Cord Blood Stem Cells by Flow Cytometry

INTRODUCTION: Stem cell laboratories measure the number of viable CD34+ and CD45+ cells in their products by flow cytometry following a standard protocol (ISHAGE). Although the ISHAGE protocol is well documented, its correct application can be challenging. Automated gating algorithms have been created to address this issue, but they have limitations and can only be used in certain conditions. Recent developments in artificial intelligence (AI) and machine learning (ML) have made these approaches more accessible and practical to use. For flow cytometry data, the Cytobank platform now offers the Automatic Gating Algorithm (AGA), an AI-ML-based feature allowing users to easily implement their own gating strategies. OBJECTIVES: The performance of the AGA based on the ISHAGE protocol gating was evaluated using cord blood post-thaw flow cytometry data. METHODS: In this study, the AGA was trained using 29 flow cytometry cord blood data files analyzed according to the ISHAGE gating strategy. Using the model, an inference analysis was conducted on two series of 12 data samples acquired from two independent operators. The enumeration of viable CD34+ cells was manually performed and compared to the AI analyses. RESULTS: The mean differences and standard deviation in vCD34+ counts between the manual and the automatic gating are 6.0% ± 2.7 for Operator #1 and 7.7% ± 2.1 for Operator #2. The correlation coefficients between vCD34+ results are 0.9981 for Operator #1 vs AI, and 0.9699 for Operator #2 vs AI. These results show that the AI inference provides results comparable to human manual gating, and are within the accepted difference of 10% between duplicates as defined by ISHAGE convention. DISCUSSION: This study provides a testing ground for using AI-ML-based automatic gating and supports its use in stem cell laboratories with acceptable performance and accuracy. The application of AI and ML for vCD34+ enumeration can potentially lead to the development of more widely applicable automatic gating algorithms. This could improve standardization by providing a common tool for analyzing flow cytometry data.