Harmonization and strengthening of Japan's biodosimetry network to support medical triage in the event of a nuclear disaster.

Journal: International journal of radiation biology
Published Date: Jul 21, 2025

Abstract

PURPOSE: The development of AI-assisted biodosimetry systems brings significant advances in cytogenetic dosimetry. The introduction of deep learning algorithms has improved the accuracy and speed of chromosome detection and classification in input images, addressing the incomplete reproducibility and time-consuming of manual evaluation. An advanced molecular cytogenetic technique, PNA-FISH, has further improved the clarity and reliability of chromosome identification. We have been developing a deep learning algorithm to automate the detection of chromosomal aberrations in PNA-FISH images, resulting in a more efficient approach to dose assessment, particularly in large-scale nuclear disasters.

Authors

  • Kotaro Ishii
    Department of Radiation Measurement and Dose Assessment, Institute for Radiological Science, National Institutes for Quantum Science and Technology, Chiba, Japan.
  • Yoshio Takashima
    Department of Radiation Measurement and Dose Assessment, Institute for Radiological Science, National Institutes for Quantum Science and Technology, Chiba, Japan.
  • Miho Akiyama
    Department of Radiation Measurement and Dose Assessment, Institute for Radiological Science, National Institutes for Quantum Science and Technology, Chiba, Japan.
  • Takako Tominaga
    Department of Radiation Emergency Management, Institute for Radiological Science, National Institutes for Quantum Science and Technology, Chiba, Japan.
  • Hiroki Kawai
    Research and Development Division, LPixel Inc., Chiyoda-ku, Tokyo, Japan.
  • Yumiko Suto
    Department of Radiation Measurement and Dose Assessment, Institute for Radiological Science, National Institutes for Quantum Science and Technology, Chiba, Japan.

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