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Updated: Jun 22, 2025

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Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning.

Wei Wang1, Kuo Chen2, Xing Ma3

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Fundamental Research
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for accurate point-of-care testing (POCT). This AI-driven UCNP-LFA strategy overcomes data limitations for reliable, real-time quantitative detection in diverse environments.

Keywords:
Internet of medical thingsLateral flow assaysPortable fluorescent sensorTransfer learningUpconverting nanoparticles

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Area of Science:

  • Nanotechnology and biosensing
  • Artificial intelligence in computer vision
  • Point-of-care diagnostics

Background:

  • Upconverting nanoparticles (UCNPs) combined with immunochromatography offer promising point-of-care testing (POCT) but face challenges like low luminescence efficiency and image noise.
  • Artificial intelligence (AI) shows significant potential in computer vision for improving data analysis and overcoming limitations in current detection techniques.

Purpose of the Study:

  • To develop a novel quantitative detection strategy by integrating AI with upconversion nanoparticle-based lateral flow assays (UCNP-LFAs).
  • To enhance the accuracy, robustness, and applicability of UCNP-LFAs for real-time quantitative detection, particularly in field settings.
  • To address data scarcity and low computing power limitations in POCT equipment.

Main Methods:

  • Utilized transfer learning to train AI models on a small, self-built database for UCNP-LFA quantitative detection.
  • Deployed trained AI models in internet of things (IoT) devices for real-time inference without extensive data preprocessing.
  • Validated the strategy using eight transfer learning models for quantitative detection of two detectors, including tests with added noise.

Main Results:

  • Achieved significant improvements in accuracy and robustness for quantitative detection using AI-trained UCNP-LFAs.
  • Demonstrated ultra-high accuracy prediction results (up to 100%) from AI models, even under strong noise conditions.
  • Showcased the strategy's applicability in field detection environments and its ability to overcome POCT equipment limitations.

Conclusions:

  • The AI-integrated UCNP-LFA strategy offers a universal, accurate, and sensitive quantitative detection method suitable for optical biosensors.
  • This approach effectively addresses practical challenges in POCT, paving the way for revolutionary advancements in the field.
  • The developed device holds significant scientific value for transforming POCT technology and commercial potential in the in vitro diagnostics (IVD) industry.