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Kernel representation-based End-to-End network-enabled decoding strategy for precise and medical diagnosis.

Qinyu Wang1, Xuewen Peng2, Niu Feng2

  • 1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China.

Journal of Hazardous Materials
|January 17, 2025
PubMed
Summary
This summary is machine-generated.

A new artificial intelligence (AI) model, CellNet, accurately detects dense and adherent targets in imaging biosensors. This AI advancement improves biomarker quantification for disease diagnosis and other applications.

Keywords:
Artificial intelligenceIn-vitro diagnoseKernel representationMicroscopic imagingProcalcitonin

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Imaging biosensors offer flexible digital analysis for biomarker quantification.
  • Deep learning faces challenges with target density and adherence in biomarker detection.
  • Accurate identification of dense and adherent targets is crucial for biosensing applications.

Purpose of the Study:

  • To introduce CellNet, a novel neural network for detecting dense targets in imaging.
  • To develop an AI-transcoding biosensing method (bs-SMART) for biomarker detection.
  • To validate CellNet's performance in identifying irregular and adherent cells.

Main Methods:

  • Developed CellNet, a neural network utilizing a shape-aware radial basis function for object kernel representation.
  • Implemented a biotin-streptavidin-based biosensing method using artificial intelligence transcoding (bs-SMART).
  • Applied CellNet to detect procalcitonin in serum samples and evaluate cell recognition.

Main Results:

  • CellNet achieved 98.39% accuracy in detecting adherent polystyrene microspheres.
  • The bs-SMART method demonstrated high accuracy and sensitivity for procalcitonin detection (LOD = 8.5 pg/mL).
  • CellNet successfully recognized irregular and adherent cells, validating its robustness.

Conclusions:

  • CellNet enhances target counting accuracy and addresses challenges in dense target detection.
  • The bs-SMART technique provides a reliable platform for accurate disease diagnosis.
  • CellNet shows significant potential for advancing medical diagnostics, food safety, and environmental monitoring.