Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Abstract

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

Next-Generation Microfluidics for Biomedical Research and Healthcare Applications

“…This integration enables microfluidic systems to autonomously adapt, optimize processes, and respond to changing conditions using cloud-based analysis and remote monitoring. 82 As a result, the connectivity and analytical capability hold potential in fields such as healthcare, environmental monitoring, and diagnostics, where real-time insights and rapid decision-making are crucial. 83 For example, microfluidic devices integrated with environmental sensors can continuously monitor water quality and transmit data for real-time analysis.…”

AI-enhanced biomedical micro/nanorobots in microfluidics

“…37 Numerous algorithms of machine learning (such as decision tree, random forest, support vector machine (SVM), k -nearest neighbors ( k -NN), convolution neural network (CNN), recurrent neural network (RNN), Transformers, graph neural network (GNN), reinforcement learning (RL), and their variants) have made remarkable accomplishments in diverse domains. 38–42 These include computer vision, natural language processing, facial recognition, robotic process automation and bioinformatics. For instance, in the field of computer vision, AI algorithms have improved the ability to interpret and understand visual data, which have a profound impact on areas like image analysis.…”

A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features

“…With the rapid development of computer technology, machine learning has demonstrated powerful data analysis capabilities, which have made great contributions to the development of many industries. Droplets of different sizes, physical properties, and concentrations are produced in processes involving droplet generation, such as microfluid [5,6], ink-jet printing [7], and spraying [8]. The study of droplet dynamics will generate a large number of valuable but complex droplet datasets.…”