WormAI: Artificial Intelligence Networks for Nematode Phenotyping

Abstract

The nematode Caenorhabditis elegans is a key model organism in biological research due to its genetic similarity to humans and its utility in studying complex processes. Traditional image analysis methods, such as those using ImageJ, are labour-intensive, which has led to the integration of AI. This study introduces an AI framework with three machine learning models: WormGAN, a Generative Adversarial Network for generating synthetic nematode images to enhance training data; WormDET, for precise movement tracking; and WormREG, for accurate anatomical measurements. Together, these tools significantly improve the efficiency and accuracy of phenotypic analysis. WormAI demonstrates substantial potential for high-throughput dataset analysis, advancing research in systems biology, drug discovery, and aging. This framework streamlines workflows, enabling faster and more precise discoveries in C. elegans studies.