Machine Learning Approaches for Defect Detection in a Microwell-based Medical Device

TL;DR

This paper presents a CNN-based machine learning method for automated defect detection in microwell-based microfluidic medical devices, aiming to improve quality control efficiency and consistency.

Contribution

The study introduces a 9-layer CNN model with dropout and regularization for high-throughput defect detection in microwell devices, reducing manual QC efforts.

Findings

Significantly increased image analysis throughput.

Improved consistency in quality control results.

Potential to replace manual defect inspection.

Abstract

Microfluidic devices offer numerous advantages in medical applications, including the capture of single cells in microwell-based platforms for genomic analysis. As the cost of sequencing decreases, the demand for high-throughput single-cell analysis devices increases, leading to more microwells in a single device. However, their small size and large quantity increase the quality control (QC) effort. Currently, QC steps are still performed manually in some devices, requiring intensive training and time and causing inconsistency between different operators. A way to overcome this issue is to through automated defect detection. Computer vision can quickly analyze a large number of images in a short time and can be applied in defect detection. Automated defect detection can replace manual inspection, potentially decreasing variations in QC results. We report a machine learning (ML) algorithm that applies a convolution neural network (CNN) model with 9 layers and 64 units, incorporating dropouts and regularizations. This algorithm can analyze a large number of microwells produced by injection molding, significantly increasing the number of images analyzed compared to manual operator, improving QC, and ensuring the delivery of high-quality products to customers.