Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos

Abstract

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new approach combining zebrafish embryo behavioral phenotyping with deep learning was used to identify and classify water pollutants. Zebrafish embryos were exposed to 17 psychoactive compounds at environmentally relevant concentrations (1 and 10 μg/L), and their locomotor behavior was recorded at 5 and 6 days post-fertilization (dpf). We constructed six distinct zebrafish locomotor track datasets encompassing various observation times and developmental stages and evaluated multiple deep learning models on these datasets. The results demonstrated that the ResNet101 model performed optimally on the 1-min track dataset at 6 dpf, achieving an accuracy of 65.35 %. Interpretability analyses revealed that the model effectively focused on the relevant locomotor track features for classification. These findings suggest that the integration of zebrafish embryo behavioral analysis with deep learning can serve as an environmentally friendly and economical method for detecting water pollutants. This approach offers a new perspective for water quality monitoring and has the potential to assist existing chemical analytical techniques in detection, thereby advancing environmental toxicology research and water pollution control efforts.