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Automated analysis of mouse rearing using deep learning.

Naoaki Sakamoto1, Masahiro Fukuda2, Yusuke Miyazaki1

  • 1Animal Radiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.

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|July 26, 2025
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Summary
This summary is machine-generated.

This study introduces a novel convolutional recurrent neural network (CRNN) model for automatically detecting mouse rearing behavior from videos. The AI model accurately quanties animal exploration and anxiety indicators, comparable to human analysis.

Keywords:
Deep learningMachine learningRearing

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Area of Science:

  • Neuroscience and behavioral science
  • Artificial intelligence in biological research

Background:

  • Rodent rearing behavior is a key indicator for assessing anxiety and exploratory tendencies in animal models.
  • Accurate quantification of rearing is crucial for behavioral neuroscience research.

Purpose of the Study:

  • To develop and validate a convolutional recurrent neural network (CRNN) model for automated detection of mouse rearing behavior.
  • To assess the model's performance against human observation and its ability to detect behavioral changes.

Main Methods:

  • Overhead videos of C57BL/6 mice were collected under varying light conditions.
  • Manual frame-by-frame labeling of rearing events was performed for supervised learning.
  • A CRNN model was trained using the labeled behavioral data.
  • Model performance was evaluated using sensitivity metrics on independent test datasets.

Main Results:

  • The CRNN model achieved a sensitivity of 89.2%, demonstrating performance comparable to human observers.
  • The model successfully detected increased rearing behavior after caffeine administration.
  • The model differentiated between diurnal and nocturnal activity patterns in mice.

Conclusions:

  • The developed CRNN model offers a reliable and automated method for quantifying mouse rearing behavior.
  • This AI-driven approach can enhance the efficiency and objectivity of behavioral assessments in preclinical research.
  • The model's ability to detect pharmacological effects and circadian variations highlights its utility in neuroscience studies.