Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

ABSTRACT: Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active-Matrix Digital Microfluidics (AM-DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi-target, collision-free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% puri

SUBMITTER: Guo Z 

PROVIDER: S-EPMC11906218 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature