Artificial intelligence enabled multi-purpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

Active-matrix electrowetting-on-dielectric (AM-EWOD) system, integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip biochemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. Conventional image processing algorithms are sensitive to factors such as lighting and noise, resulting in limited generalizability. They lack the capability to autonomously learn features and often require manual parameter adjustments. In this work, we developed a series of deep learning algorithms based on an AM-EWOD system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the “one-to-two” strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor..., , , # Artificial intelligence enabled multi-purpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

Uniformity analysis
Success rate calculation
Single-cell recognition

## Description of the data and file structure

**The 1cell.jpg,2cell.jpg,3cell.jpg,4cell.jpg** are the results of the cell detection model.

**The 1_out_s.jpg,2_out_s.jpg,3_out_s.jpg** are the results of the droplet segmention model.

**The 2.jpg,3.jpg,4.jpg** are the results of the droplet detection model.

## Sharing/Access information

The source code, pretrained weights, and samples associated with this paper are available in Github via the repository (). support and more information are available from Z.J. <[e-mail]>.

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## Code/Software

**best.pt** is the weight of the cell detection model.

**C2PC_BLOCK.py** and **draw_s.py** are the python source code file.