vivoAI^TM

vivoVerse has developed two AI models that turn high-resolution C. elegans images into quantitative toxicity data. vivoBodySeg automatically recognizes and outlines individual worms, allowing measurements such as body length, area, and volume to be calculated quickly and consistently; in published testing, its body segmentation closely matched expert analysis while reducing analysis time by about 140-fold. EmbryoMAE-Det extends this approach to reproductive health by automatically finding embryos inside each worm and classifying them by developmental stage. Trained using approximately 48,000 annotated embryos, the model replaces an otherwise labor-intensive manual counting process and enables embryo data from large toxicity studies to be analyzed roughly 1,000 times faster. Together, these models allow vivoVerse to rapidly measure both developmental and reproductive changes across thousands of animals while reducing manual effort and observer-to-observer variability.

Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing

Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism’s development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate novel assays. C. elegans have emerged as NAMs for rapid toxicity testing because of its biological relevance and suitability to high throughput studies. However, current low-resolution and labor-intensive methodologies prohibit its application for sub-lethal DevTox studies at high throughputs. With the recent advent of the large-scale microfluidic device, vivoChip, we can now rapidly collect 3D high-resolution images of ~ 1000 C. elegans from 24 different populations. While data collection is rapid, analyzing thousands of images remains time-consuming. To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments C. elegans in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36 GB data per device, phenotyping multiple body parameters within 35 min on a desktop PC. This analysis is ~ 140 × faster than the manual analysis. This ML approach delivers highly reproducible DevTox parameters (4–8% CV) to assess the toxicity of chemicals with high statistical power.

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Machine Learning-Accelerated Analysis of In Utero Embryo Phenotyping in C. elegans for Reproductive Toxicity Assessment

Predictive new approach methodologies (NAMs) for developmental and reproductive toxicity (DART) assessment are increasingly needed as reliance on conventional mammalian studies decreases and chemical safety evaluation demands continue to expand. Whole-organism NAMs, including Caenorhabditis elegans, provide a scalable non-mammalian strategy because they preserve conserved biological pathways within an intact physiological system. We recently developed vivoDART, a rapid and repeatable C. elegans assay that quantiĮes in utero embryo development and overcomes key limitations of traditional labor-intensive, multiday C. elegans DART workflows. However, despite its robustness and reproducibility, vivoDART still requires manual analysis of tens of thousands of embryos per chemical, a process that is time-consuming and prone to user-dependent variability. To address this bottleneck, we developed EmbryoMAE-Det, a machine-learning framework trained on ~48,000 manually segmented embryos from 1,547 worms. The model combines self-supervised masked autoencoder pretraining with supervised object detection and classification to identify embryos within the C. elegans uterus and classify them by developmental stage. EmbryoMAE-Det achieved high accuracy (mAP = 88.7%), with AP values of 92.8% and 84.7% for early- and late-stage embryo counts, respectively. Model-derived embryo counts showed low variability with CV%s for technical replicates below 10.4%, sufficient statistical power to detect changes as small as 5-18%, and EC50 values statistically indistinguishable from those obtained by manual scoring. The fully automated workflow reduces analysis time by 1,000×. In summary, this work establishes an integrated whole-organism imaging and machine-learning platform for rapid, reproducible, and high-content DART evaluation using C. elegans as a NAM.

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