Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

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Abstract

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Abstract

Smartphone-based mobile health platforms have drawn increasing attention from researchers developing point-of-care testing devices. Here the authors summarize recent progress and future directions of approaches combining microfluidics and artificial intelligence.

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Most cited references 193

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Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis.

Wei Gao, Sam Emaminejad, Hnin Nyein … (2016)

Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi … (2021)

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Wearable and flexible electronics for continuous molecular monitoring

Yiran Yang, Wei Gao (2019)

Wearable/flexible chemical sensors enable continuous molecular monitoring and provide an individual's dynamic health information at the molecular level. Wearable biosensors have received tremendous attention over the past decade owing to their great potential in predictive analytics and treatment toward personalized medicine. Flexible electronics could serve as an ideal platform for personalized wearable devices because of their unique properties such as light weight, low cost, high flexibility and great conformability. Unlike most reported flexible sensors that mainly track physical activities and vital signs, the new generation of wearable and flexible chemical sensors enables real-time, continuous and fast detection of accessible biomarkers from the human body, and allows for the collection of large-scale information about the individual's dynamic health status at the molecular level. In this article, we review and highlight recent advances in wearable and flexible sensors toward continuous and non-invasive molecular analysis in sweat, tears, saliva, interstitial fluid, blood, wound exudate as well as exhaled breath. The flexible platforms, sensing mechanisms, and device and system configurations employed for continuous monitoring are summarized. We also discuss the key challenges and opportunities of the wearable and flexible chemical sensors that lie ahead.

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Author and article information

Contributors

Xingcai Zhang:

ORCID: http://orcid.org/0000-0001-7114-1095

24212602@qq.com

Xiaojun Feng: xfeng@mail.hust.edu.cn

Bi-Feng Liu:

ORCID: http://orcid.org/0000-0002-2135-0873

bfliu@mail.hust.edu.cn

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 11 March 2023

Publication date PMC-release: 11 March 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 1341

Affiliations

[1 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, , Huazhong University of Science and Technology, ; Wuhan, 430074 China

[2 ]GRID grid.38142.3c, ISNI 000000041936754X, School of Engineering and Applied Sciences, , Harvard University, ; Cambridge, MA 02138 USA

Author information

Xingcai Zhang http://orcid.org/0000-0001-7114-1095

Bi-Feng Liu http://orcid.org/0000-0002-2135-0873

Article

Publisher ID: 36017

DOI: 10.1038/s41467-023-36017-x

PMC ID: 10007670

PubMed ID: 36906581

SO-VID: 3298fb54-4084-4c0f-a6ed-8b7a140d0a3e

License:

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 23 March 2022

Date accepted : 10 January 2023

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ScienceOpen disciplines: Uncategorized

Keywords: computational biology and bioinformatics,laboratory techniques and procedures,materials for devices,biomedical engineering,microfluidics

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ScienceOpen disciplines: Uncategorized

Keywords: computational biology and bioinformatics, laboratory techniques and procedures, materials for devices, biomedical engineering, microfluidics

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

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Most cited references 193

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Wearable and flexible electronics for continuous molecular monitoring

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