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Related Experiment Video

Updated: May 1, 2026

Use of the Open Field Maze to Measure Locomotor and Anxiety-like Behavior in Mice
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OpenFieldAI - new open-source AI based software for tracking rodents and training open field test models.

Muzammil Kabier1, Shamili Mariya Varghese2, K V Athira2

  • 1Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India.

Journal of Neuroscience Methods
|January 4, 2026
PubMed
Summary

OpenFieldAI is a free, open-source software for analyzing rodent behavior in open field tests. It uses AI for accurate tracking, offering a user-friendly interface for researchers and reliable results validated against commercial software.

Keywords:
Artificial intelligenceAutomationBehavioral methodNeurobehaviorOpen field testOpenFieldAITracking softwareYOLO

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Area of Science:

  • Neuroscience
  • Behavioral Science
  • Computational Biology

Background:

  • Open field test (OFT) is crucial for rodent behavioral analysis, assessing locomotion, exploration, and anxiety.
  • Current analysis methods (manual or commercial software) have limitations like observer bias, high cost, and steep learning curves.
  • Automated systems are preferred for objectivity and efficiency in scientific research.

Purpose of the Study:

  • To introduce OpenFieldAI, a novel, open-source, Python-based software for automated analysis of rodent behavior in OFT.
  • To provide researchers with a user-friendly, cost-effective alternative to existing commercial software.
  • To ensure the reliability and accuracy of OpenFieldAI through validation against established methods.

Main Methods:

  • OpenFieldAI utilizes the YOLO algorithm for accurate detection and tracking of rodents within the OFT arena.
  • The software accepts pre-recorded or live video inputs, calculating key behavioral parameters (speed, distance, time in ROI, entries/exits).
  • Features include automatic ROI detection, manual ROI drawing, heat maps, and path tracking for comprehensive neurobehavioral insights.

Main Results:

  • OpenFieldAI demonstrated high reliability, with Pearson correlation coefficients above 0.9 when compared to the commercial ANY-maze software for key parameters.
  • The software provides essential outputs like centroid graphs, heat maps, and line paths, aiding in detailed scientific interpretation.
  • Validation confirmed the software's accuracy in measuring total distance, speed, and time/entries in central and peripheral regions.

Conclusions:

  • OpenFieldAI offers a reliable, accurate, and accessible automated solution for rodent behavioral analysis in open field tests.
  • Its intuitive GUI and open-source nature make advanced behavioral research more accessible to a wider range of scientists.
  • The software provides valuable neurobehavioral data, overcoming the limitations of manual analysis and costly commercial alternatives.