An artificial intelligence-assisted digital microfluidic system for multistate droplet control.

ABSTRACT: Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recogn

SUBMITTER: Guo K 

PROVIDER: S-EPMC11427450 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature