Objectification of evaluation criteria in microscopic agglutination test using deep learning.

Journal: Journal of microbiological methods
PMID: 38754481

Abstract

We aim to objectify the evaluation criteria of agglutination rate estimation in the Microscopic Agglutination Test (MAT). This study proposes a deep learning method that extracts free leptospires from dark-field microscopic images and calculates the agglutination rate. The experiments show the effect of objectification with real pictures.

Authors

  • Risa Nakano
    Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
  • Yuji Oyamada
    Graduate School of Engineering, Tottori University, Japan. Electronic address: oyamada@tottori-u.ac.jp.
  • Ryo Ozuru
    Department of Microbiology & Immunology, Faculty of Medicine, Fukuoka University, Japan. Electronic address: ozuru@fukuoka-u.ac.jp.
  • Michinobu Yoshimura
    Department of Microbiology & Immunology, Faculty of Medicine, Fukuoka University, Japan.
  • Kenji Hiromatsu
    Department of Microbiology & Immunology, Faculty of Medicine, Fukuoka University, Japan.

Keywords

External Resources

Popular Topics
Recent Journals