Share Email Print
cover

Proceedings Paper

Deep learning-based sensing of viruses using a particle aggregation assay (Conference Presentation)

Paper 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.

Paper Details

Date Published: 9 March 2020
PDF
Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300S (9 March 2020); doi: 10.1117/12.2546861
Show Author Affiliations
Yichen Wu, Univ. of California, Los Angeles (United States)
Aniruddha Ray, Univ. of California, Los Angeles (United States)
Qingshan Wei, Univ. of California, Los Angeles (United States)
Alborz Feizi, Univ. of California, Los Angeles (United States)
Xin Tong, Univ. of California, Los Angeles (United States)
Eva Chen, Univ. of California, Los Angeles (United States)
Yi Luo, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11230:
Optics and Biophotonics in Low-Resource Settings VI
David Levitz; Aydogan Ozcan, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray