Machine Learning-Driven Innovations in Microfluidics

Abstract

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Keywords: biosensing technology; droplet generation; machine learning; microfluidic devices.

Figure 1

A timeline depicting the integration of microfluidics and machine learning biosensing applications. (A) The timeline of technological advancements in microfluidics and machine learning, highlighting key milestones and their integration. (B) Specific breakthroughs and applications in intelligent microfluidics.

Figure 2

Leveraging machine learning for microfluidic device design optimization. (A) A schematic of the convolutional neural network (CNN) used in this work. Adapted with permission [65], copyright 2017, Scientific Reports. (B) The workflow of the developed design automation tool for flow-focusing droplet generators, called DAFD. Adapted with permission [64], copyright 2021, Nature Communications. (C) A workflow chart and a description of methods used in the building of the machine learning-guided design of an experiment based on the decision tree method. Adapted with permission [66], copyright 2023, Chemical Engineering Research and Design.

Figure 3

Machine learning-enhanced control and design of droplets in microfluidic systems. (A) Prediction of droplet flow dynamics using deep neural networks. Adapted with permission [65], copyright 2019, Scientific Reports. (B) Real-time droplet classification for multijet monitoring. Adapted with permission [64], copyright 2022, ACS Applied Materials & Interfaces. (C) Design automation of single- and double-emulsion droplets through machine learning. Adapted with permission [66], copyright 2024, Nature Communications.

Figure 4

Machine learning-enabled real-time monitoring and analysis within microfluidic platforms. (A) Microscopy-based label-free imaging flow cytometry with real-time image processing. Adapted with permission [90], copyright 2017, Scientific Reports. (B) Viral aerosol detection employing microfluidics and machine learning for rapid classification. Adapted with permission [91], copyright 2024, ACS Nano. (C) Skin-interfaced microfluidic patch with machine learning-based image processing for sweat biomarker analysis. Adapted with permission [92], copyright 2022, Advanced Materials Technologies.