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9 March 2020 Deep learning-based sensing of viruses using a particle aggregation assay (Conference Presentation)
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Abstract
We demonstrate an automatic, high-throughput and high-sensitivity particle aggregation-based sensor that uses wide-field, compact and cost-effective lens-less microscopy, powered by deep neural networks. In this method, the post-reaction assay is imaged by a snapshot hologram over a wide field-of-view (20mm²). Using a deep learning-based holographic reconstruction, all the particle clusters are simultaneously reconstructed in ~30s. Using this method, we demonstrated accurate and rapid readout of an immunoassay to detect herpes simplex virus, which affects >50% of the adults in US, and achieved a clinically-relevant detection limit (~ 5viruses/µL). This method can be broadly used to quantify other particle-aggregation based immunoassays.
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Yichen Wu, Aniruddha Ray, Qingshan Wei, Alborz Feizi, Xin Tong, Eva Chen, Yi Luo, and Aydogan Ozcan "Deep learning-based sensing of viruses using a particle aggregation assay (Conference Presentation)", Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300S (9 March 2020); https://doi.org/10.1117/12.2546861
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