Artificial Intelligence Integration with Microfluidics

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B:Biology and Biomedicine".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4524

Special Issue Editors

Department of Biomedical Engineering, 4F, Engineering Building, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 333323, Taiwan
Interests: organ-on-chip; heterogenous microfluidics; microfluidics automation; machine learning for biomedical image analysis; clinical laboratory science
Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
Interests: cell-based microanalysis; electrotaxis; microfluidic biochip development and applications; microarray technologies; laser micro machining
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Special Issue Information

Dear Colleagues,

Advances in micro- and nano-fabrication, as well as applying fundamental fluid dynamics in microscale to biomedical applications, have yielded great works under the discipline of microfluidics. Micro total analysis systems, also known as labs-on-a-chip systems, have been created through miniaturizing and integrating microfluidics, as well as control and detection components, providing high-throughput and multiplex measurements as well as manipulations of analytes in a small configuration. Microphysiological systems also enable the control of a microenvironment where fundamental biological processes can be studied in a quantitative manner, eventually providing reliable and clinically translatable results that would in turn relieve the need of animal models in drug discovery and fundamental research. However, as microsystems have become more complex, system design and fabrication have become more dependent on experience. Moreover, the increasing amount of data provided by advanced microfluidic platforms has made data analysis the bottleneck of applying microsystems in research.
In recent years, advances in deep convolutional neural networks in the field of deep learning has successfully solved many of the Big Data analytical problems, such as pattern recognition, classification, and segmentation of targets in conventionally complex data collected from microsystems, thereby integrating the fourth boom of artificial intelligence with microfluidics.

In this Special Issue, we would like to highlight the benefits and possibilities brought by a microfluidic system integrated with machine learning and deep learning techniques for fundamental biomedical discovery, as well as practical applications.

We look forward to receiving your submissions.

Dr. Paul Hsieh-Fu Tsai
Prof. Dr. Ji-Yen Cheng
Guest Editors

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Keywords

  • artificial intelligence system
  • computer-aided chip design
  • smart micro- and nano-fabrication
  • image analysis and metrology
  • big data mining
  • pattern recognition
  • cell culture
  • microphysiological system
  • cell analysis and manipulation
  • droplet generation and sorting
  • biosensing applications

Published Papers (1 paper)

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Review

30 pages, 9153 KiB  
Review
Microsystem Advances through Integration with Artificial Intelligence
Micromachines 2023, 14(4), 826; https://doi.org/10.3390/mi14040826 - 08 Apr 2023
Cited by 6 | Viewed by 3786
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, [...] Read more.
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Microfluidics)
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