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Detection of microplastics in fish using computed tomography and deep learning
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CT scanning combined with deep learning neural networks enabled non-destructive, automated detection and localization of microplastics in fish with high accuracy, overcoming the contamination risk and time-consuming nature of conventional dissection-based methods.
Marine organisms have been observed ingesting microplastic particles, with field analyses indicating fibers and fragments as prevalent forms. Current microplastic detection methods are mainly time-consuming, susceptible to cross-contamination, and expensive. Furthermore, these techniques, being disruptive, do not allow for the exact localization of the microplastic in the sample. This study proposes a new approach using Computed Tomography (CT scan) and Artificial Intelligence for the automatic and non-destructive detection of microplastics in fishes and other species based on the combination of several factors, such as density and shape. The advantages of this methodology include accurate identification of plastic localization, a low risk of cross-contamination, rapid processing, automatic tomographic measurement, efficient data processing, cost-effectiveness, and a high cost-benefit ratio. The herein results highlight how artificial intelligence applied to conventional techniques can significantly improve precision and efficiency in microplastic research. Indeed, the semantic segmentation model clearly recognized the presence of 100 % of the plastic particles, both in their location and in their volume, accelerating the identification process and surpassing the limitations of traditional spectral analysis methodologies.
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Researchers developed MicroDetect-Net, a deep learning model trained to detect microplastics in clam blood, demonstrating a pathway toward automated detection in human blood samples as plastic production exceeds 368 million tons annually and microplastics are found across all environmental and biological matrices.
Detection of Microplastic Ingestion in the Human Body Using Deep Learning Technique
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Researchers applied convolutional neural networks trained in MATLAB to detect and quantify microplastic contamination in high-resolution tissue images, demonstrating that deep learning can automate the identification of plastic particles in biological samples.
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
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Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.
Automatic Detection of Microplastics in the Aqueous Environment
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Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Deep learning based approach for automated characterization of large marine microplastic particles
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A deep learning approach using Mask R-CNN was trained on 3,000 images of marine microplastic particles to automatically locate, classify, and segment particles by shape categories including fiber, fragment, pellet, and rod. The model achieved high accuracy and outperformed manual visual inspection for characterizing large marine microplastic datasets.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.