Park Jinseok, Kim Yang Woo, Jeon Hee-Jae
Department of Smart Health Science and Technology, Kangwon National University, Chuncheon 24341, Republic of Korea.
Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea.
Biosensors (Basel). 2024 Dec 13;14(12):613. doi: 10.3390/bios14120613.
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.
微流控设备通过在各种应用中实现对微小流体体积的精确操控，彻底改变了生物传感技术。本综述探讨了将机器学习（ML）融入微流控生物传感器的设计、制造和应用，重点阐述了ML算法如何通过提高设计精度、操作效率以及复杂诊断数据集的管理来提升性能。将微流控技术与ML相结合，催生了能够实现实验工作流程自动化、进行实时数据分析并支持明智决策的智能系统。本文对由ML推动的健康诊断、环境监测和合成生物学领域的最新进展进行了批判性审视。本综述突出了ML增强型微流控系统的变革潜力，为这一快速发展领域的未来发展轨迹提供了见解。
