Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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Deep Neural Network Technology for Analyzing PDA Colorimetric Transition Sensors in Pathogen Detection

병원균 검출용 PDA 색 전이 센서 분석을 위한 심층신경망 기술

  • Junhyeon Jeon (Department of Mechanical Engineering, Inha University) ;
  • Huisoo Jang (Industrial Science and Technology Research Institute, Inha University) ;
  • Mingyeong Shin (Department of Food and Nutrition, Inha University) ;
  • Tae-Joon Jeon (Department of Biological Engineering, Department of Biological Sciences and Bioengineering, Biohybrid Systems Research Center, Inha University) ;
  • Sun Min Kim (Department of Mechanical Engineering, Department of Biological Sciences and Bioengineering, Biohybrid Systems Research Center, Inha University)
  • Received : 2024.04.11
  • Accepted : 2024.05.19
  • Published : 2024.07.31
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Abstract

In this study, we propose a novel approach for rapid and accurate pathogen detection by integrating Polydiacetylene (PDA) hydrogel sensors with advanced deep learning algorithms and visualization techniques. PDA hydrogel sensors exhibit a color transition in the presence of pathogens, enabling straightforward and quick pathogen detection. We developed a reliable pathogen detection system that combines deep neural network algorithms with color quantification technology for image-based analysis. This image-based system retains the ease of pathogen detection offered by PDA sensors while deriving quantified color standards to overcome the limitations of human visual assessment, enhancing reliability. This advancement contributes to public health and the development and application of pathogen detection technology.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2021R1A2C2003571, RS-2023-00207801).

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