Developing a Machine Learning Algorithm for Identifying Abnormal Urothelial Cells: A Feasibility Study.

Journal: Acta cytologica
PMID: 33022673

Abstract

INTRODUCTION: Urine cytology plays an important role in diagnosing urothelial carcinoma (UC). However, urine cytology interpretation is subjective and difficult. Morphogo (ALAB, Boston, MA, USA), equipped with automatic acquisition and scanning, optical focusing, and automatic classification with convolutional neural network has been developed for bone marrow aspirate smear analysis of hematopoietic diseases. The goal of this preliminary study was to determine the feasibility of developing a machine learning algorithm on Morphogo for identifying abnormal urothelial cells in urine cytology slides.

Authors

  • Zhihui Zhang
    Department of Pathology, Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), Beijing, China.
  • Xinyan Fu
    Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, China.
  • Jiwei Liu
    Department of Oncology, The First Hospital of Dalian Medical University (FHDMU), Dalian, China.
  • Zhen Huang
    Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, Hangzhou, China.
  • Natalia Liu
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California, USA.
  • Fengqi Fang
    Department of Oncology, The First Hospital of Dalian Medical University, Dalian, China.
  • Jianyu Rao
    Department of Pathology and Laboratory Medicine, UCLA, David Geffen School of Medicine, Los Angeles, California, USA, JRao@mednet.ucla.edu.

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