Artificial intelligence and 3D modeling for understanding microplastic behavior in living organisms

Application of Artificial Intelligence to Improve Microplastic Identification

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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.

Intelligent technology-enabled comprehensive research on microplastics: Detection, fate, and ecological effects

Integration of bio-inspired algorithms with machine learning for modeling microplastic contamination transport processes in natural aquatic habitats using morphological features

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Scientists built a computer model that can accurately predict how microplastic particles move through rivers, lakes, and oceans based on their shape and size. This matters because understanding where these tiny plastic bits travel and accumulate, including in water sources we may eventually drink or in seafood we eat, can help authorities track pollution hotspots and create smarter policies to keep our water supplies cleaner.

A Machine-Learning Model for Investigating Microplastics Source–Receptor Relationships in Aquatic Environments

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Researchers developed a machine-learning model to trace where microplastics in aquatic environments come from and where they end up, potentially giving scientists and regulators a more powerful tool to identify pollution sources and prioritize cleanup efforts.

Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning

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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.