Data from: 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 the droplet splitting success rates over 18 different
AM-EWOD chips, and a 97.7% splitting success rate was observed. The
results indicated that the model precision was 99.980% and the model
recall was 99.976% through manual verification. In addition, we used an
improved YOLOv8 model to detect single cells in nanoliter droplets
effectively. In comparison with manual verification, the results showed
that the model achieved a precision of 99.260% and a recall of 99.193%. By
leveraging an artificial intelligence enabled smart detection system,
AM-EWOD system has shown great potential as a ubiquitous platform for true
lab-on-a-chip.