Data-Driven Predictive Modeling of Microfluidic Cancer Cell Separation Using a Deterministic Lateral Displacement Device

TL;DR

This paper presents a machine learning-based framework for optimizing deterministic lateral displacement microfluidic devices to improve cancer cell separation, reducing computational costs and enhancing design precision.

Contribution

It introduces a data-driven approach combining machine learning with microfluidic design to automate and optimize DLD device configurations for cancer cell isolation.

Findings

Machine learning models accurately predict particle trajectories.

Optimized device parameters improve cancer cell separation efficiency.

Framework enables high-throughput, cost-effective DLD design.

Abstract

Deterministic Lateral Displacement (DLD) devices are widely used in microfluidics for label-free, size-based separation of particles and cells, with particular promise in isolating circulating tumor cells (CTCs) for early cancer diagnostics. This study focuses on the optimization of DLD design parameters, such as row shift fraction, post size, and gap distance, to enhance the selective isolation of lung cancer cells based on their physical properties. To overcome the challenges of rare CTC detection and reduce reliance on computationally intensive simulations, machine learning models including gradient boosting, k-nearest neighbors, random forest, and multilayer perceptron (MLP) regressors are employed. Trained on a large, numerically validated dataset, these models predict particle trajectories and identify optimal device configurations, enabling high-throughput and cost-effective DLD design. Beyond trajectory prediction, the models aid in isolating critical design variables, offering a systematic, data-driven framework for automated DLD optimization. This integrative approach advances the development of scalable and precise microfluidic systems for cancer diagnostics, contributing to the broader goals of early detection and personalized medicine.