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. 2019 May 31;9:8114. doi: 10.1038/s41598-019-44556-x

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Using neural networks to measure fluidic properties from droplet flow patterns: (a) Schematic of the microfluidic channels (depth is 30 μm). (b) Image of droplet generation. Oil flows from the side channels and the water-IPA flows from the middle channel. (c) The two-phase pattern of the droplet generation in a microfluidic device contains information about the fluid and the flow properties. Neural networks are used to extract these properties while the main process stays intact.