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

Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a ro...

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Autores principales: Ganjalizadeh, Vahid, Meena, Gopikrishnan G., Stott, Matthew A., Hawkins, Aaron R., Schmidt, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034896/
https://www.ncbi.nlm.nih.gov/pubmed/36959357
http://dx.doi.org/10.1038/s41598-023-31694-6
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author Ganjalizadeh, Vahid
Meena, Gopikrishnan G.
Stott, Matthew A.
Hawkins, Aaron R.
Schmidt, Holger
author_facet Ganjalizadeh, Vahid
Meena, Gopikrishnan G.
Stott, Matthew A.
Hawkins, Aaron R.
Schmidt, Holger
author_sort Ganjalizadeh, Vahid
collection PubMed
description Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.
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spelling pubmed-100348962023-03-23 Machine learning at the edge for AI-enabled multiplexed pathogen detection Ganjalizadeh, Vahid Meena, Gopikrishnan G. Stott, Matthew A. Hawkins, Aaron R. Schmidt, Holger Sci Rep Article Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices. Nature Publishing Group UK 2023-03-23 /pmc/articles/PMC10034896/ /pubmed/36959357 http://dx.doi.org/10.1038/s41598-023-31694-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ganjalizadeh, Vahid
Meena, Gopikrishnan G.
Stott, Matthew A.
Hawkins, Aaron R.
Schmidt, Holger
Machine learning at the edge for AI-enabled multiplexed pathogen detection
title Machine learning at the edge for AI-enabled multiplexed pathogen detection
title_full Machine learning at the edge for AI-enabled multiplexed pathogen detection
title_fullStr Machine learning at the edge for AI-enabled multiplexed pathogen detection
title_full_unstemmed Machine learning at the edge for AI-enabled multiplexed pathogen detection
title_short Machine learning at the edge for AI-enabled multiplexed pathogen detection
title_sort machine learning at the edge for ai-enabled multiplexed pathogen detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034896/
https://www.ncbi.nlm.nih.gov/pubmed/36959357
http://dx.doi.org/10.1038/s41598-023-31694-6
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