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Related Experiment Video

Updated: Jun 12, 2025

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
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A miniaturized liver function detection system with machine learning enhancing strategy.

Yang Zeng1, Bianzheng Wang2, Jie Cheng3

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

Biosensors & Bioelectronics
|June 10, 2025
PubMed
Summary
This summary is machine-generated.

A new, low-cost system uses machine learning to quickly detect liver function by measuring alanine aminotransferase (ALT). This portable device offers accurate results in 3 minutes, overcoming limitations of traditional methods in resource-limited settings.

Keywords:
ALT colorimetric analysisConvolutional neural networkGrayscale processingLiver function detection

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Area of Science:

  • Biomedical Engineering
  • Clinical Chemistry
  • Artificial Intelligence in Medicine

Background:

  • Serum alanine aminotransferase (ALT) is a key biomarker for liver function and acute liver injury (ALI).
  • Current clinical methods for ALT detection are often hindered by high costs, slow turnaround times, and complex technical demands, limiting their use in resource-limited areas.
  • There is a need for accessible, rapid, and accurate liver function testing solutions.

Purpose of the Study:

  • To develop and validate a miniaturized, low-cost liver function detection system.
  • To integrate machine learning algorithms for enhanced quantitative and semi-quantitative ALT detection.
  • To provide a rapid and reliable alternative for liver function assessment, particularly in underserved regions.

Main Methods:

  • A portable detection instrument with precise temperature control (37 ± 0.4 °C) was developed.
  • Quantitative ALT detection utilized a grayscale processing algorithm.
  • Semi-quantitative ALT detection employed a convolutional neural network (CNN) model comprising four convolutional, activation, and pooling layer blocks, validated via the hold-out method.

Main Results:

  • The system achieved a quantitative limit of detection of 5.47 U/L and a measurable range of 6–395 U/L.
  • Quantitative detection showed a high linear correlation (R=0.9930) with automated analyzers; semi-quantitative detection achieved 96.97% accuracy.
  • Results were obtained within 3 minutes, with minimal interference (<8%) and good reliability (CV <10%).

Conclusions:

  • The developed miniaturized system offers a low-cost, rapid, and accurate method for ALT measurement.
  • Machine learning integration enhances detection capabilities, making it suitable for diverse clinical settings.
  • This technology presents a convenient and efficient alternative for liver function testing, addressing limitations of current diagnostic tools.