Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Bioengineering & Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/btm2.10428 |
| _version_ | 1797777258295853056 |
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| author | Yuxuan Tang Fei Duan Aiwu Zhou Pojchanun Kanitthamniyom Shaobo Luo Xuyang Hu Xudong Jiang Shawn Vasoo Xiaosheng Zhang Yi Zhang |
| author_facet | Yuxuan Tang Fei Duan Aiwu Zhou Pojchanun Kanitthamniyom Shaobo Luo Xuyang Hu Xudong Jiang Shawn Vasoo Xiaosheng Zhang Yi Zhang |
| author_sort | Yuxuan Tang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T23:01:34Z |
| format | Article |
| id | doaj.art-3b1eaf529cb9499292bd880be96bd03c |
| institution | Directory Open Access Journal |
| issn | 2380-6761 |
| language | English |
| last_indexed | 2024-03-12T23:01:34Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioengineering & Translational Medicine |
| spelling | doaj.art-3b1eaf529cb9499292bd880be96bd03c2023-07-19T09:59:03ZengWileyBioengineering & Translational Medicine2380-67612023-07-0184n/an/a10.1002/btm2.10428Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnosticsYuxuan Tang0Fei Duan1Aiwu Zhou2Pojchanun Kanitthamniyom3Shaobo Luo4Xuyang Hu5Xudong Jiang6Shawn Vasoo7Xiaosheng Zhang8Yi Zhang9School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore SingaporeSchool of Mechanical and Aerospace Engineering Nanyang Technological University Singapore SingaporeSingapore Center for 3D Printing, School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore SingaporeSchool of Mechanical and Aerospace Engineering Nanyang Technological University Singapore SingaporeSchool of Microelectronics Southern University of Science and Technology Shenzhen ChinaChina‐Singapore International Joint Research Institute Guangzhou ChinaSchool of Electronic and Electrical Engineering Nanyang Technological University Singapore SingaporeNational Center for Infectious Disease Tan Tock Seng Hospital Singapore SingaporeSchool of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu ChinaSchool of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu ChinaAbstract 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.https://doi.org/10.1002/btm2.10428artificial intelligenceIn vitro diagnosticsmagnetic digital microfluidics |
| spellingShingle | Yuxuan Tang Fei Duan Aiwu Zhou Pojchanun Kanitthamniyom Shaobo Luo Xuyang Hu Xudong Jiang Shawn Vasoo Xiaosheng Zhang Yi Zhang 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 artificial intelligence In vitro diagnostics magnetic digital microfluidics |
| title | Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics |
| title_full | Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics |
| title_fullStr | Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics |
| title_full_unstemmed | Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics |
| title_short | Image‐based real‐time feedback control of magnetic digital microfluidics by artificial intelligence‐empowered rapid object detector for automated in vitro diagnostics |
| title_sort | image based real time feedback control of magnetic digital microfluidics by artificial intelligence empowered rapid object detector for automated in vitro diagnostics |
| topic | artificial intelligence In vitro diagnostics magnetic digital microfluidics |
| url | https://doi.org/10.1002/btm2.10428 |
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