Synergizing microfluidics and machine learning for next generation intelligent Lab-on-a-Chip devices

Abstract

This review explores the synergistic integration of microfluidics and machine learning, highlighting their combined potential to transform conventional Lab-on-a-Chip systems into state-of-the-art intelligent and autonomous platforms. In brief, the paper highlights the importance of this synergistic approach and potential research avenues in which this technology will be beneficial. Different methods of data collection to build a machine learning model are discussed briefly. Then the paper discusses the use of machine learning in microfluidic device design and highlights automatic tools like Design Automation of Fluid Dynamics for droplet diameter prediction and flow sculpting via convolutional neural networks. The paper recounts machine learning driven control strategies for real-time flow behavior and droplet size manipulation, employing reinforcement learning, Bayesian optimization, and convolutional neural network-based feedback systems. The article further explains the varied applications of intelligent microfluidics in biotechnology, such as cell sorting, cytometry, and disease diagnostics which uses classical machine learning models and deep learning. In chemistry-related research, machine learning aids in optimizing reactions and predicting concentrations, while in biosensing, it enables enhanced signal processing and biomarker detection. Finally, the article also covers the growing impact of machine learning in organ- and organoid-on-chip platforms, and emphasizes their combined role in personalized medicine and disease modeling. Collectively, these advancements underscore the pivotal role of machine learning in enhancing microfluidic functionality, scalability, and clinical relevance, and fosters mutually beneficial relations between the two emerging fields.