• Journal of the Korean Society of Visualization
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• v.22 no.2
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• pp.27-34
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• 2024

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.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.397-403
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• 2002

Urine glucose monitoring system is a self-monitoring system that display the glucose level by non-invasive measurement method. In this paper, We developed a noninvasive urine glucose monitoring system that improved defects of urine glucose measurement with a colorimeter method and invasive blood glucose measurement method. This system consist of bio-chemical sensor for urine glucose measurements, signal detecting part, digital and signal analysis part, display part and power supplying part. The developed bio-chemical sensor for the measurement of urine glucose has good reproducibility, convenience of handing and can be mass-produced with cheap price. To evaluate the performance of the developed system, We performed the evaluation of confidence about the detection of glucose level by a comparison between a standard instrument in measuring glucose level and the developed system using standard glucose solutions mixed with urine. Standard error was 2.85282 from the evaluation of confidence based on regression analysis. Also, In analysis of S.D(standard deviation) and C.V(coefficient of validation) that are important parameters to evaluate system using bio-chemical sensor, S.D was 10% which falls under clinically valid value, 15%, and C.V was under 5%. Consequently from the above results, compared to blood glucose measurement, the system performance is satisfactory.