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EPISODE 24: Worms on the Chip – Machine Learning Meets Toxicology 2025-08-13 Andrew DuPlissis – guest , Evan Hegarty – guest , Veeren Chauhan – host , Abhishri Medewar – guest 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 once Achieves near-human segmentation accuracy (Dice score: 97.8%) Measures subtle toxicity effects like changes in body size and gut autofluorescence Identifies EC10 and LOAEL values with high precision Uses few-shot learning to adapt the model to new worm shapes and sizes This 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! This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch. 📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected] AI/ML	WOrM Podcast: Whole Organism Analytics Podcast Listen

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 once Achieves near-human segmentation accuracy (Dice score: 97.8%) Measures subtle toxicity effects like changes in body size and gut autofluorescence Identifies EC10 and LOAEL values with high precision Uses few-shot learning to adapt the model to new worm shapes and sizes

This 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!

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

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