Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Jun Hee Choi, Wookyoung Jang, Yong Jun Lim, Seok Joon Mun, Ki Wan Bong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of HMPs with diverse graphical codes. However, previous related studies have found the use of plain particles as data to be disadvantageous for accurate analyses of HMPs loaded with functional nanomaterials. Furthermore, the manual data annotation method used in existing approaches is highly labor-intensive and time-consuming. In this study, we present an efficient deep-learning-based analysis of encoded HMPs with diverse graphical codes and functional nanomaterials, utilizing the auto-annotation and synthetic data mixing methods for model training. The auto-annotation enhanced the throughput of dataset preparation up to 0.11 s/image. Using synthetic data mixing, a mean average precision of 0.88 was achieved in the analysis of encoded HMPs with magnetic nanoparticles, representing an approximately twofold improvement over the standard method. To evaluate the practical applicability of the proposed automatic analysis strategy, a single-image analysis was performed after the triplex immunoassay for the preeclampsia-related protein biomarkers. Finally, we accomplished a processing throughput of 0.353 s per sample for analyzing the result image.

Original languageEnglish
Pages (from-to)3158-3166
Number of pages9
JournalACS Sensors
Volume8
Issue number8
DOIs
Publication statusPublished - 2023 Aug 25

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (20018111, Development of super-fast multiplex technology for the examination of diagnosis of infectious disease and in-body response test) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) and the Engineering Research Center of Excellence Program through the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIP) (RS-2023-00207833) and Korea Government (MSIT) (RS-2023-00253210) also supported by a Korea TechnoComplex Foundation Grant.

Publisher Copyright:
© 2023 American Chemical Society.

Keywords

  • auto-annotation
  • deep learning
  • graphical encoding
  • hydrogel microparticle
  • multiplex immunoassay
  • synthetic data

ASJC Scopus subject areas

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes

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