Deep Learning–Assisted Digital Microfluidic Platform for Automated CRISPR/Cas12 Detection of <i>Mycobacterium tuberculosis</i>

Abstract

CRISPR-based diagnostics offer high sensitivity and specificity for nucleic acid detection, but their translation to point-of-care use remains limited by dependence on benchtop instrumentation, manual reagent handling, and costly optical components. To address these limitations, this study developed a fully integrated, low-cost digital microfluidic (DMF) system capable of automating the complete CRISPR/Cas12 workflow using on-board electronics and smartphone-based imaging. The system incorporates a programmable electrode array capable of precise droplet actuation for sample preparation and reagent mixing, a closed-loop heating module that maintains a stable reaction temperature of 39 °C, and a compact 3D-printed fluorescence imaging unit for end-point signal acquisition. To facilitate rapid and objective interpretation, we implemented a YOLOv11 deep learning model to classify fluorescence outputs into positive or negative results, achieving a mean average precision at 50% of 0.889. We applied the platform to the detection of Mycobacterium tuberculosis (MTB) DNA. The on-chip CRISPR assays reliably detected MTB across a dynamic range from 1 ng/μL down to 10–8 ng/μL, with no signal observed in no-template controls. Overall, the device delivers analytical performance comparable to conventional tube-based CRISPR assays while offering portability, reduced user intervention, and minimized risk of handling errors. These results highlight the potential of the integrated DMF–CRISPR system as a practical and accessible solution for point-of-care molecular diagnostics.