Automated Embryo Sorting in Zebrafish Facilities: Performance Benchmarking Against Manual Processing

Area of Science:

• * Comparative analysis of automated and manual methods in zebrafish embryo management.
• * Application of artificial intelligence (AI) in high-throughput biological screening.

Background:

• * Zebrafish (Danio rerio) are crucial model organisms in biomedical research.
• * Manual handling of large zebrafish embryo populations presents efficiency and ergonomic challenges.

Purpose of the Study:

• * To evaluate an AI-powered automated embryo sorting system against manual methods.
• * To assess the efficiency, accuracy, and reliability of automated embryo processing in operational settings.

Main Methods:

• * A large-scale comparison involving 117,956 zebrafish embryos.
• * Evaluation of an AI system for simultaneous counting, quality classification, and GFP fluorescence detection.
• * Benchmarking against manual counting and sorting procedures.

Main Results:

• * Automated system processed embryos at 17.3/min vs. manual at 24.5-25.9/min (p < 0.001).
• * Observed 13.41% systematic undercounting (p = 0.004) and a 50.8% false negative rate for GFP detection.
• * Limited correlation between automated classifications and developmental outcomes (p > 0.05).

Conclusions:

• * The AI system offers convenience and ergonomic advantages but requires further algorithm refinement for accuracy.
• * Context-dependent implementation is advised: automation for high-volume tasks, manual validation for critical applications.
• * Continuous improvement is expected through expanded training data and user feedback.