Functions and applications of artificial intelligence in droplet microfluidics

Abstract

Droplet microfluidics emerges as a powerful technology to perform high-throughput experiment, while artificial intelligence (AI) serves as a functional tool to analyze a large set of multiplex data. Their convergence...

Direct laser writing-enabled 3D printing strategies for microfluidic applications

“…, “soft lithography” using polydimethylsiloxane (PDMS)—represent one of the most pervasive approaches for manufacturing microfluidic channels in research settings. 107–109 Most such approaches, however, rely on the aforementioned conventional microfabrication techniques that are ill suited for applications that demand sophisticated microchannel geometries. 110 As a result, the replicated microchannels and/or microstructures typically comprise “2.5D” geometries, such as rectangular microchannels and microfeatures with relatively straight sidewalls and a single, uniform height.…”

Development and future of droplet microfluidics

“…197 The large dataset generated by droplet microfluidics favors AI to train its models, while the constructed models can feed back microfluidic systems to achieve high degrees of accuracy and sensitivity. 198 The enhanced detection will allow more pathogens to be accurately identified, thus facilitating the application of droplet microfluidics in a much broader range of medical tests.…”

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

“…By harnessing AI algorithms, these devices can extract valuable insights from the vast amount of data generated by microfluidic systems, leading to improved diagnostics, personalized treatment strategies, and real-time monitoring of health conditions. 255,263…”

“…Striking the right balance in model complexity is crucial to prevent issues such as overfitting or underfitting. 263 The intricacies of microfluidic data and the diverse nature of physiological measurements necessitate AI models that are sophisticated enough to capture relevant patterns yet not overly complex to hinder practical deployment. Ethical considerations add an additional layer of complexity.…”