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February 22, 2026Cytometry Part A

Automated Gating of CD34 + Cells in Cord Blood: Performance Evaluation of a Machine Learning‐Based ISHAGE Protocol

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Authors

CSCarl SimardDFDiane FournierPTPatrick Trépanier

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Overview

Evaluation of a machine learning approach automates CD34+ cell enumeration in cord blood, reducing variability.

Key Points

  • Evaluate the efficacy of a machine learning-based automated gating algorithm for CD34+ cells in cord blood.
  • Training on 29 manually gated FCS files
  • Application to raw flow cytometry data
  • Performance evaluation using Z-scores, correlations, and Bland–Altman analysis
  • Comparison with manual gating from nine laboratories
  • Assessment of intraclass correlation coefficients (ICCs)
  • AI1 remained within ± 2 SD of human consensus across 12 samples
  • AI1 correlated strongly with manual gating (r = 0.991), while AI2 showed lower correlation (r = 0.968)
  • Minimal bias and narrow limits of agreement were found for AI1 in Bland–Altman analysis
  • ICC showed high reliability for AI1, especially with Lab1 (ICC = 0.995)
  • AI2 exhibited greater variability compared to human and human comparisons.

Cite This Study

Simard et al. (2026) studied this question.

synapsesocial.com/papers/699a9de0482488d673cd424bhttps://doi.org/10.1002/cyto.a.70017
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