ArticleACS measurement science au2026
Deep Learning-Assisted Digital Microfluidic Platform for Automated CRISPR/Cas12 Detection of
Article in ACS measurement science au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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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
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