中文版 | English
Title

Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics

Author
Corresponding AuthorZhang, Xiaosheng; Zhang, Yi
Publication Years
2022-10-01
DOI
Source Title
EISSN
2380-6761
Abstract
In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)-empowered MDM platform with image-based real-time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI-empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI-based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error-free droplet operations for a wide range of automated IVD applications.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China[62074029] ; Sichuan Science and Technology Program[2022JDTD0020]
WOS Research Area
Biotechnology & Applied Microbiology ; Engineering ; Pharmacology & Pharmacy
WOS Subject
Biotechnology & Applied Microbiology ; Engineering, Biomedical ; Pharmacology & Pharmacy
WOS Accession No
WOS:000868997000001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:3
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406511
DepartmentSUSTech Institute of Microelectronics
Affiliation
1.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore, Singapore
2.Nanyang Technol Univ, Singapore Ctr 3D Printing, Sch Mech & Aerosp Engn, Singapore, Singapore
3.Southern Univ Sci & Technol, Sch Microelect, Shenzhen, Peoples R China
4.China Singapore Int Joint Res Inst, Guangzhou, Peoples R China
5.Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
6.Tan Tock Seng Hosp, Natl Ctr Infect Dis, Singapore, Singapore
7.Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu, Peoples R China
Recommended Citation
GB/T 7714
Tang, Yuxuan,Duan, Fei,Zhou, Aiwu,et al. Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics[J]. BIOENGINEERING & TRANSLATIONAL MEDICINE,2022.
APA
Tang, Yuxuan.,Duan, Fei.,Zhou, Aiwu.,Kanitthamniyom, Pojchanun.,Luo, Shaobo.,...&Zhang, Yi.(2022).Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics.BIOENGINEERING & TRANSLATIONAL MEDICINE.
MLA
Tang, Yuxuan,et al."Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics".BIOENGINEERING & TRANSLATIONAL MEDICINE (2022).
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