Deep-Learning-Assisted Integrated Microfluidic Platform for Automated Analysis of EV RNA Mutations

Extracellular vesicles (EVs) carry key genetic biomarkers, such as somatic mutations, making them valuable targets for non-invasive cancer diagnostics. However, standard workflows that isolate EVs and detect low-abundance mutations typically require multi-step manual operations, leading to sample loss and potential cross-contamination. Here, we present an integrated microfluidic platform combined with a deep learning algorithm for the continuous processing and automated droplet analysis of EV-derived RNA mutations. The microfluidic chip integrates magnetic bead-based EV capture, online lysis, droplet generation, and arrayed RT-droplet digital PCR (RT-ddPCR) on a single device. To streamline signal readout, a deep learning framework based on Cellpose and K-means clustering was implemented to process individual fluorescence images, enabling droplet detection, background removal, gray-scale intensity extraction, and automatic positive/negative droplet classification. The system showed a linear response across a wide range of original-sample inputs (R² = 0.9950). The platform was evaluated using EV samples derived from three cancer cell lines (NCI-H1975, SK-LU-1, and HepG2) and the detection of EGFR L858R and KRAS mutations were achieved. By coupling continuous sample processing with automated image interpretation, this platform offers a practical reference for integrated EV nucleic acid analysis for liquid-biopsy applications.