Microsystem Advances through Integration with Artificial Intelligence

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, 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.

Point-of-care tests for human papillomavirus detection in uterine cervical samples: A review of advances in resource-constrained settings

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Can Artificial Intelligence Accelerate Fluid Mechanics Research?

“…Another popular subject is fluid property estimation with ML algorithms, a promising alternative to prohibitively expensive experimental measurements.Most reviews have exploited historical data from experiments or simulations to predict fluid behaviour and calculate the properties of interest [87][88][89][90][91][92][93][94][95][96]. Furthermore, heat transfer applications, concerning the addition of nanoparticles in simple fluids [97][98][99][100][101][102] or the investigation of flows in microfluidic channels [103][104][105][106][107][108] play a central role. Of equal significance, though with fewer instances, several recent reviews refer to aerodynamics [109][110][111][112][113], geosciences, geoengineering and environmental subjects [114][115][116][117][118][119][120][121][122][123][124][125][126], energy applications [127][128][129][130], biological processes…”

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

“…These devices leverage AI algorithms to process the data obtained from microfluidic components, allowing for more sophisticated and adaptive functionality. 23…”

“…22 However, there are challenges to be addressed, including the development of algorithms that can accurately interpret and make sense of the vast amount of data generated by microfluidic wearable sensors, and ensuring that the technology is accessible and affordable to all patients. 23…”