In this episode, we explore a high-tech twist on developmental toxicology. Researchers have combined microfluidic engineering with machine learning to automate the analysis of thousands of C. elegans for chemical toxicity testing — no anaesthetics or low-res imaging required.Using the vivoChip device and a custom ML model called vivoBodySeg, the team:Captures 3D images of ~1000 worms from 24 populations at onceAchieves near-human segmentation accuracy (Dice score: 97.8%)Measures subtle toxicity effects like changes in body size and gut autofluorescenceIdentifies EC10 and LOAEL values with high precisionUses few-shot learning to adapt the model to new worm shapes and sizesThis platform slashes analysis time by 140× and sets a new benchmark for high-throughput New Approach Methodologies (NAMs) in toxicology.📖 Based on the research article:“Machine learning-based analysis of microfluidic device immobilised C. elegans for automated developmental toxicity testing”Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, et al.Published in Scientific Reports (2025)🔗 https://doi.org/10.1038/s41598-024-84842-x🎧 Subscribe to the WOrM Podcast for more stories where whole-organism biology meets cutting-edge tech!🔗 www.veerenchauhan.com

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