Machine learning-enabled multiplexed microfluidic sensors
File(s)
Author(s)
Dabbagh, Sajjad Rahmani
Rabbi, Fazle
Dogan, Zafer
Yetisen, Ali Kemal
Tasoglu, Savas
Type
Journal Article
Abstract
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.
Date Issued
2020-12-11
Date Acceptance
2020-12-01
Citation
Biomicrofluidics, 2020, 14 (6)
ISSN
1932-1058
Publisher
American Institute of Physics
Journal / Book Title
Biomicrofluidics
Volume
14
Issue
6
Copyright Statement
©2020 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Biomicrofluidics 14, 061506 (2020); doi: 10.1063/5.0025462
and may be found at 10.1063/5.0025462
and may be found at 10.1063/5.0025462
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000598100400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biochemical Research Methods
Biophysics
Nanoscience & Nanotechnology
Physics, Fluids & Plasmas
Biochemistry & Molecular Biology
Science & Technology - Other Topics
Physics
LOW-COST
NEURAL-NETWORKS
MICROSCOPY
POINT
RECOGNITION
PREDICTION
FRAMEWORK
VERSATILE
GENOME
THREAD
Publication Status
Published
Article Number
ARTN 061506
Date Publish Online
2020-12-11