Analytica chimica acta · 2025
Darkfield illumination enhancing artificial-intelligence-assisted digital microfluidics for on-site pathogen detection.
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Song Zerui, Xu Jingsong, Guo Kunlun, Wu Zhanli, Feng Boyi, Tang Wanxin, Wang Hua, Gu Zhen, Wang Huifeng
Аннотация
BACKGROUND: Artificial-intelligence (AI) based computer vision emerges as an important part in the digital microfluidic (DMF) system for realization of high-level automatic control of droplets for on-site pathogen detection at the nano-micro scale. However, the low optical contrast and indistinct of droplets in the oil media by conventional imaging approaches limits the performance of the AI-based droplet recognition and feature extractions in practical applications.
RESULTS: Herein, we present an integrated Indium-Tin-Oxide (ITO)-DMF platform featuring a switchable dual-mode optical system. The system utilizes a darkfield imaging mode for robust, AI-driven process control, which is enabled by a darkfield illumination-based transmissive configuration that significantly enhances droplet contrast. This enhancement proved essential for deep-learning models, yielding exceptional performance in object detection (mAP50 = 98.3 %) and semantic segmentation (mIoU = 98.2 %). This high-fidelity visual feedback is critical for precise droplet manipulation, which precedes endpoint analysis where the system switches to an epi-fluorescence mode. As a functional validation, this AI-assisted control enabled the automated, multi-step detection of Mycoplasma pneumoniae from clinical samples in under 11 min on-chip reaction time, achieving 100 % concordance with qPCR results.
SIGNIFICANCE: This study demonstrates that integrating darkfield illumination into DMF platforms is a powerful and essential strategy to empower AI-driven visual feedback for robust, automated process control. The proposed ITO-DMF platform with darkfield illumination is promising to be a versatile and highly extensible solution for AI-assisted DMF applications, such as point-of-care testing, DNA storage, single cell analysis and synthetic biology.
RESULTS: Herein, we present an integrated Indium-Tin-Oxide (ITO)-DMF platform featuring a switchable dual-mode optical system. The system utilizes a darkfield imaging mode for robust, AI-driven process control, which is enabled by a darkfield illumination-based transmissive configuration that significantly enhances droplet contrast. This enhancement proved essential for deep-learning models, yielding exceptional performance in object detection (mAP50 = 98.3 %) and semantic segmentation (mIoU = 98.2 %). This high-fidelity visual feedback is critical for precise droplet manipulation, which precedes endpoint analysis where the system switches to an epi-fluorescence mode. As a functional validation, this AI-assisted control enabled the automated, multi-step detection of Mycoplasma pneumoniae from clinical samples in under 11 min on-chip reaction time, achieving 100 % concordance with qPCR results.
SIGNIFICANCE: This study demonstrates that integrating darkfield illumination into DMF platforms is a powerful and essential strategy to empower AI-driven visual feedback for robust, automated process control. The proposed ITO-DMF platform with darkfield illumination is promising to be a versatile and highly extensible solution for AI-assisted DMF applications, such as point-of-care testing, DNA storage, single cell analysis and synthetic biology.