MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in...

Area of Science:

• Neuroscience
• Toxicology
• Pharmacology

Background:

• Substance use disorders, especially opioid addiction, present a significant global health challenge.
• Rodent models are crucial for studying opioid withdrawal but rely on subjective manual behavioral assessments.
• Current methods lack objectivity, reproducibility, and scalability for modern drug toxicity evaluation.

Purpose of the Study:

• Introduce MWB_Analyzer, an automated, high-throughput system for objective morphine withdrawal assessment in rats.
• Enhance toxicological assessments of CNS-active substances via scalable behavioral phenotyping.
• Improve the precision and standardization of addiction research and drug safety profiling.

Main Methods:

• Integrated multi-angle video capture and real-time audio signal processing.
• Employed a YOLO-based architecture for machine learning-driven classification of behaviors.
• Utilized behavior-specific duration thresholds and standardized toxicological protocols.

Main Results:

• MWB_Analyzer achieved >95% reduction in redundant frame processing, enhancing computational efficiency.
• Demonstrated high classification accuracy: >94% for video and >92% for audio-based events.
• Sensitive differentiation between dosage groups revealed clear dose-response relationships.

Conclusions:

• MWB_Analyzer provides a robust, reproducible, and objective platform for automated opioid withdrawal evaluation in rodents.
• The system enhances throughput and precision for addiction research and neurotoxicological profiling.
• Supports toxicological investigations, preclinical evaluations, and regulatory assessment of CNS-active therapeutics.