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Related Experiment Video

Updated: May 6, 2026

Lensless Fluorescent Microscopy on a Chip
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Darkfield illumination enhancing artificial-intelligence-assisted digital microfluidics for on-site pathogen

Zerui Song1, Jingsong Xu2, Kunlun Guo1

  • 1Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education & State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Analytica Chimica Acta
|December 2, 2025
PubMed
Summary

This study introduces a novel Indium-Tin-Oxide (ITO)-digital microfluidic (DMF) platform that uses darkfield imaging to improve artificial intelligence (AI) control for pathogen detection. The AI-assisted system achieves high accuracy and rapid detection of Mycoplasma pneumoniae.

Keywords:
Darkfield illuminationDigital microfluidicsDroplet processingMycoplasma pneumoniae

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Area of Science:

  • Microfluidics
  • Artificial Intelligence
  • Computer Vision
  • Pathogen Detection

Background:

  • Digital microfluidic (DMF) systems utilize AI for automated droplet control in pathogen detection.
  • Conventional imaging methods struggle with low droplet contrast in oil media, limiting AI performance.
  • Enhanced droplet visibility is crucial for accurate AI-based recognition and feature extraction in microfluidic applications.

Purpose of the Study:

  • To develop an integrated Indium-Tin-Oxide (ITO)-DMF platform with enhanced optical capabilities for AI-driven droplet manipulation.
  • To improve the contrast and recognition of droplets in oil media for more robust AI control.
  • To validate the platform's efficacy in automated pathogen detection using AI-assisted visual feedback.

Main Methods:

  • Integrated an ITO-DMF platform with a switchable dual-mode optical system.
  • Employed a darkfield imaging mode with transmissive illumination to enhance droplet contrast.
  • Utilized deep-learning models for object detection and semantic segmentation based on darkfield imaging.
  • Validated the system with automated, multi-step detection of Mycoplasma pneumoniae from clinical samples.

Main Results:

  • The darkfield imaging mode significantly enhanced droplet contrast, enabling high-performance AI models.
  • Achieved excellent results in object detection (mAP50 = 98.3%) and semantic segmentation (mIoU = 98.2%).
  • Demonstrated automated detection of Mycoplasma pneumoniae in under 11 minutes with 100% concordance to qPCR.

Conclusions:

  • Integrating darkfield illumination into DMF platforms is essential for robust AI-driven visual feedback and automated process control.
  • The proposed ITO-DMF platform offers a versatile and extensible solution for AI-assisted microfluidic applications.
  • This technology shows promise for point-of-care testing, DNA storage, single-cell analysis, and synthetic biology.