Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening

Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep learning models trained using labeled image data offer a more objective and efficient alternative. As part of the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) project, which aims to provide a scientific basis for programmatic decisions on the routine use of zebrafish in toxicological evaluations, we developed deep-learning classification and segmentation models to support image-based assessments. Using labeled SEAZIT image data from embryos exposed to various chemicals for 5 days, we trained classification models to detect 20 distinct types of larval morphological changes. We also developed segmentation models to identify and measure larval regions of interest. In a baseline binary classification task distinguishing normal embryos from those with any morphological change, our multi-view convolutional neural network (MVCNN) achieved an F1 score of 0.88, demonstrating strong overall performance. Among classifiers for specific morphological changes, eight achieved F1 scores above 0.70. Grouping related abnormalities further improved performance, with five out of seven grouped classifiers reaching F1 scores near 0.80. Segmentation models for most of the regions of interest (9 out of 11) have an Intersection over Union (IoU) score of at least 0.80. Together, these models facilitate rapid, standardized and reliable evaluation of larval morphological changes in zebrafish images, reducing the need for subjective manual review and enhancing reproducibility in development toxicity screening.