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Related Experiment Video

Updated: Aug 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Machine learning at the edge for AI-enabled multiplexed pathogen detection.

Vahid Ganjalizadeh¹, Gopikrishnan G Meena¹, Matthew A Stott²

¹School of Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA.

Scientific Reports

|March 24, 2023

Summary

This summary is machine-generated.

We developed a Deep Neural Network (DNN) for real-time multiplex biomarker detection. This AI approach enables accurate, amplification-free pathogen identification on low-cost, compact diagnostic devices.

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Last Updated: Aug 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Molecular Diagnostics

Background:

Real-time multiplexed biomarker detection is vital for point-of-use diagnostics.
Challenges include signal variability and low signal-to-noise ratios at the detection limit.

Purpose of the Study:

To create a robust target identification scheme for multiplex detection of single particles and molecular biomarkers.
To validate the scheme using bacterial nucleic acid detection.

Main Methods:

Utilized a Deep Neural Network (DNN) combined with wavelet particle detection and Short-Time Fourier Transform analysis.
Implemented the DNN on an AI-specific edge device (Google Coral Dev board).
Validated using optofluidic detection of Klebsiella Pneumoniae nucleic acids.

Main Results:

Demonstrated amplification-free, real-time 3x multiplexing with high specificity and sensitivity.
Achieved an excellent classification accuracy of 99.8% for pathogen detection.
Showcased robust performance with varying fluorescence signal quality.

Conclusions:

A minimalistic DNN design optimized for edge devices offers a robust framework for accurate pathogen detection.
This approach facilitates the development of compact, low-cost diagnostic devices for point-of-care applications.
The method is suitable for real-time identification of biomarkers with diverse signal characteristics.