Relationship Between Artificial Intelligence-Based Cell Detection and Cytomorphological Variations Induced by Cell-Processing Solutions: Usefulness of Data Augmentation in Artificial Intelligence Cytology.

Journal: Acta cytologica
Published Date: Jul 21, 2025

Abstract

INTRODUCTION: Variations in cytomorphology due to differences in specimen preparation conditions hinder the implementation of artificial intelligence (AI) in cytology. In addition, small-scale research and insufficient datasets pose challenges. In this study, we aimed to examine the relationship between cytomorphological variations induced by cell-processing solutions and AI-based cell detection accuracy, and to demonstrate the usefulness of data augmentation in AI cytology.

Authors

  • Nanako Sakabe
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Yuma Yoshizaki
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Kenta Fukuda
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Shouichi Sato
    Clinical Engineering, Faculty of Medical Sciences, Juntendo University, Urayasu, Japan.
  • Katsuhide Ikeda
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.

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