Multi-modal pathogen identification method, device, equipment and medium

By combining microfluidic chips and multimodal deep learning models, the problems of long pathogen detection cycles and low accuracy in existing technologies have been solved, enabling rapid and accurate pathogen identification, which is applicable to fields such as medical care and environmental monitoring.

CN121171367AActive Publication Date: 2025-12-19TIANJIN PUHENG KANGTAI TECH CO LTD
3 Cites 0 Cited by

Patent Information

Application Number
CN202511724525.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-24
Publication Date
2025-12-19
Estimated Expiration
2045-11-24

AI Technical Summary

Technical Problem

In existing technologies, when detecting pathogens using surface-enhanced Raman spectroscopy, the detection cycle is long and the accuracy of identification is low for mixed samples or complex environments. The spectral differences are small and the detection is significantly affected by background interference.

Method used

The sample to be tested was mixed with Raman scattering enhanced particles using a microfluidic chip, and then physically separated based on particle size differences. The pathogen types were predicted using a multimodal deep learning model, and two-dimensional data analysis was performed by combining Raman spectral information and outlet distribution information.

Benefits of technology

It significantly shortens the time for pathogen identification, improves identification efficiency and accuracy, has a wide range of applications, and is suitable for fields such as medical care and environmental monitoring, reducing identification errors under a single data dimension.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a multi-mode pathogen identification method, device, equipment and medium, and relates to the technical field of microfluidics, the method comprises the following steps: obtaining pathogen sample data corresponding to each outlet in a plurality of outlets of a microfluidic chip; the pathogen sample data comprises Raman scattering spectrum information and outlet distribution information; the Raman scattering spectrum information is obtained by performing surface enhanced Raman spectrum processing on pathogen group data, and the pathogen group data is output after mixing a sample to be detected and Raman scattering enhanced particles in the micro-fluidic chip and performing physical separation based on particle size difference; the particle sizes of the pathogen population data output by the outlets are different; and inputting pathogen sample data into the trained multi-modal deep learning model to obtain a pathogen type prediction result. According to the invention, the identification accuracy of pathogen types in mixed samples or complex environments is improved.
Need to check novelty before this filing date? Find Prior Art