High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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November 2020
Review Article|
December 11 2020
Machine learning-enabled multiplexed microfluidic sensors
Sajjad Rahmani Dabbagh
;
Sajjad Rahmani Dabbagh
1
Department of Mechanical Engineering, Koç University
, Sariyer, Istanbul 34450, Turkey
2
Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University
, Sariyer, Istanbul 34450, Turkey
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Fazle Rabbi
;
Fazle Rabbi
1
Department of Mechanical Engineering, Koç University
, Sariyer, Istanbul 34450, Turkey
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Zafer Doğan
;
Zafer Doğan
3
Department of Electrical and Electronics Engineering, Koç University
, Sariyer, Istanbul 34450, Turkey
4
Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University
, Sariyer, Istanbul 34450, Turkey
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Ali Kemal Yetisen;
Ali Kemal Yetisen
5
Department of Chemical Engineering, Imperial College London
, London SW7 2AZ, United Kingdom
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Savas Tasoglu
Savas Tasoglu
a)
1
Department of Mechanical Engineering, Koç University
, Sariyer, Istanbul 34450, Turkey
2
Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University
, Sariyer, Istanbul 34450, Turkey
6
Boğaziçi Institute of Biomedical Engineering, Boğaziçi University
, Çengelköy, Istanbul 34684, Turkey
7
Koc University Research Center for Translational Medicine, Koç University
, Sariyer, Istanbul 34450, Turkey
a)Author to whom correspondence should be addressed: stasoglu@ku.edu.tr
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a)Author to whom correspondence should be addressed: stasoglu@ku.edu.tr
Biomicrofluidics 14, 061506 (2020)
Article history
Received:
August 15 2020
Accepted:
December 01 2020
Citation
Sajjad Rahmani Dabbagh, Fazle Rabbi, Zafer Doğan, Ali Kemal Yetisen, Savas Tasoglu; Machine learning-enabled multiplexed microfluidic sensors. Biomicrofluidics 1 November 2020; 14 (6): 061506. https://doi.org/10.1063/5.0025462
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