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

Zebrafish (Danio rerio) are widely used in biomedical research due to their genetic similarity to humans and rapid development. Efficient management of large embryo populations is essential for experimental reproducibility and high-throughput screening, yet manual counting and sorting methods are labor-intensive, time-consuming, and pose ergonomic risks including musculoskeletal disorders. We evaluated a commercially available AI-powered automated embryo sorting system against manual methods across 117,956 embryos to assess efficiency, accuracy, and reliability in operational facility conditions. The automated system performed simultaneous counting and quality classification at 17.3 embryos/min, while manual counting alone achieved 24.5–25.9 embryos/min (p < 0.001). The automated system provided one-pass processing convenience, though at lower instantaneous throughput than manual counting. With significant ergonomic benefits. However, performance analysis revealed 13.41% systematic undercounting (p = 0.004), limited correlation between automated classifications and developmental outcomes (survival, hatching, deformity rates p > 0.05), and 50.8% false negative rate for GFP fluorescence detection in transgenic embryos. These limitations reflect the current state of machine learning algorithm training rather than fundamental technological constraints. The system’s AI architecture enables continuous improvement as algorithms encounter diverse embryo populations and incorporate user feedback through customizable training platforms. Our large-scale operational evaluation provides quantitative performance benchmarks for facilities considering automation adoption and contributes valuable real-world training data for algorithm refinement. We recommend context-dependent implementation: automated processing for high-volume routine workflows where speed and ergonomic benefits are prioritized, combined with manual validation for transgenic line maintenance and accuracy-critical applications. As an early-generation commercial platform with machine learning at its core, the system demonstrates clear improvement pathways through expanded training datasets and user-contributed algorithm optimization.