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Microsystem Advances through Integration with Artificial Intelligence

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 k...

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Autores principales: Tsai, Hsieh-Fu, Podder, Soumyajit, Chen, Pin-Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141994/
https://www.ncbi.nlm.nih.gov/pubmed/37421059
http://dx.doi.org/10.3390/mi14040826
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author Tsai, Hsieh-Fu
Podder, Soumyajit
Chen, Pin-Yuan
author_facet Tsai, Hsieh-Fu
Podder, Soumyajit
Chen, Pin-Yuan
author_sort Tsai, Hsieh-Fu
collection PubMed
description 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.
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spelling pubmed-101419942023-04-29 Microsystem Advances through Integration with Artificial Intelligence Tsai, Hsieh-Fu Podder, Soumyajit Chen, Pin-Yuan Micromachines (Basel) Review 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. MDPI 2023-04-08 /pmc/articles/PMC10141994/ /pubmed/37421059 http://dx.doi.org/10.3390/mi14040826 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Tsai, Hsieh-Fu
Podder, Soumyajit
Chen, Pin-Yuan
Microsystem Advances through Integration with Artificial Intelligence
title Microsystem Advances through Integration with Artificial Intelligence
title_full Microsystem Advances through Integration with Artificial Intelligence
title_fullStr Microsystem Advances through Integration with Artificial Intelligence
title_full_unstemmed Microsystem Advances through Integration with Artificial Intelligence
title_short Microsystem Advances through Integration with Artificial Intelligence
title_sort microsystem advances through integration with artificial intelligence
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141994/
https://www.ncbi.nlm.nih.gov/pubmed/37421059
http://dx.doi.org/10.3390/mi14040826
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