Machine learning for microfluidic design and control. Issue 16 (29th July 2022)

• Record Type: Journal Article
• Authors: McIntyre, David; Lashkaripour, Ali; Fordyce, Polly; Densmore, Douglas
• Is Part Of: Lab on a chip. Volume 22:Issue 16(2022)
• Page Start: 2925
• Page End: 2937
• Publication Date: 2022-07-29
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d2lc00254j
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : In this review article, we surveyed the applications of machine learning in microfluidic design and microfluidic control. Abstract : Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.