Deep Learning-Enabled Unbiased Precision Toxicity Assessment of Zebrafish Organ Development

Precise assessment of toxicological effects remains a key bottleneck in biomedical and environmental health assessments. Traditional toxicology relies on macroscopic end points and manual image analysis, which limit sensitivity to structural damage and introduce subjective bias. We developed an automated deep learning approach based on U-Net for the precise assessment of toxic effects and established a general framework for objective toxicological analysis. Our U-Net model can perform pixel-level segmentation and morphological quantification on thousands of biological images in 1 min without bias. This developed model was then applied to distinguish size-dependent developmental toxicity induced by Ag⁺, 15 nm, and 100 nm silver nanoparticles (AgNPs) in zebrafish, including the photoreceptor cell layer, inner plexiform layer, skeletal muscle, and spinal cord, which revealed previously undetectable size-dependent and organ-specific toxicity disparities that conventional analytical approaches failed to resolve. The method has the potential to be widely applied to the toxicity assessment of other emerging materials and contaminants. Our model displays great potential to improve toxicity assessment accuracy, efficiency, and reproducibility, providing a scalable application for precise toxicological assessments, including imaging analysis and standardization of assessment processes.