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Related Experiment Video

Updated: Sep 13, 2025

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

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Published on: December 5, 2019

Artificial Intelligence-Assisted Visualized Microspheres for Biochemical Analysis: From Encoding to Decoding.

Yang Zhou¹, Peng Lu², Yiping Chen^3,2

¹Institute of Biopharmaceutical and Health Engineering (IBHE), Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China.

Accounts of Chemical Research

|August 1, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances biochemical analysis using encoded microspheres for multiplexed detection. This AI-assisted visualized microsphere technology enables rapid, sensitive quantification of targets like proteins and bacteria.

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Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles

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Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles

Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles

Published on: October 10, 2013

Area of Science:

Biochemical analysis
Artificial intelligence (AI) in biosensing
Optical multiprobe technology

Background:

Biochemical analysis is crucial for disease diagnosis, food safety, and environmental monitoring.
Traditional methods face limitations in speed, sensitivity, and multiplexing capabilities.
AI offers advanced capabilities for processing large image datasets in biochemical assays.

Purpose of the Study:

To summarize the development of AI-assisted visualized microspheres for biosensing.
To highlight unique encoding-decoding strategies and biochemical approaches.
To demonstrate AI's role in accurate, high-speed quantification of multiple targets.

Main Methods:

Preparation of encoded fluorescent microspheres with specific biorecognition molecules.
Development of biosensing platforms integrating various biochemical sensing approaches (immunoassays, CRISPR, etc.).
Customized AI decoding algorithms (computer vision, machine learning, deep learning) for image processing.
Integration with portable optical imaging devices, including AI-integrated smartphones.

Main Results:

Demonstrated AI-assisted accurate quantification of multitarget concentrations through microsphere counting.
Achieved rapid and sensitive multiplexed detection of proteins, bacteria, viruses, and antibiotics.
Enabled point-of-care testing (POCT) using portable imaging devices and AI algorithms.

Conclusions:

AI-assisted visualized microsphere technology provides an affordable, user-friendly approach for biochemical analysis.
The integration of advanced biosensing and AI drives significant advancements in multiplexed detection.
Future work focuses on enhanced encoding capacity, lightweight decoding apps, and automated analysis systems.