Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning
The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress towa...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2023-07-01
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| Series: | Fundamental Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266732582200200X |
| _version_ | 1797774759974404096 |
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| author | Wei Wang Kuo Chen Xing Ma Jinhong Guo |
| author_facet | Wei Wang Kuo Chen Xing Ma Jinhong Guo |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress toward practical applications. Recently, artificial intelligence (AI) has demonstrated powerful representational learning and generalization capabilities in computer vision. We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for the quantitative detection of commercial internet of things (IoT) devices. This universal UCNPs quantitative detection strategy combines high accuracy, sensitivity, and applicability in the field detection environment. By using transfer learning to train AI models in a small self-built database, we not only significantly improved the accuracy and robustness of quantitative detection, but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment. Then, the trained AI model was deployed in IoT devices, whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results. We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset. The AI quickly provided ultra-high accuracy prediction results (some models could reach 100% accuracy) even when strong noise was added. Simultaneously, the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors. We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics (IVD) industry. |
| first_indexed | 2024-03-12T22:25:54Z |
| format | Article |
| id | doaj.art-9dea55af23cc47af98ee4f380dfb6ba2 |
| institution | Directory Open Access Journal |
| issn | 2667-3258 |
| language | English |
| last_indexed | 2024-03-12T22:25:54Z |
| publishDate | 2023-07-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Fundamental Research |
| spelling | doaj.art-9dea55af23cc47af98ee4f380dfb6ba22023-07-22T04:53:05ZengKeAi Communications Co. Ltd.Fundamental Research2667-32582023-07-0134544556Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learningWei Wang0Kuo Chen1Xing Ma2Jinhong Guo3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Software Engineering, Chongqing University of Posts and Telecommunications,Chongqing 400065, ChinaSchool of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China; Corresponding authors.The M.O.E. Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Corresponding authors.The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress toward practical applications. Recently, artificial intelligence (AI) has demonstrated powerful representational learning and generalization capabilities in computer vision. We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for the quantitative detection of commercial internet of things (IoT) devices. This universal UCNPs quantitative detection strategy combines high accuracy, sensitivity, and applicability in the field detection environment. By using transfer learning to train AI models in a small self-built database, we not only significantly improved the accuracy and robustness of quantitative detection, but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment. Then, the trained AI model was deployed in IoT devices, whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results. We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset. The AI quickly provided ultra-high accuracy prediction results (some models could reach 100% accuracy) even when strong noise was added. Simultaneously, the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors. We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics (IVD) industry.http://www.sciencedirect.com/science/article/pii/S266732582200200XUpconverting nanoparticlesLateral flow assaysTransfer learningInternet of medical thingsPortable fluorescent sensor |
| spellingShingle | Wei Wang Kuo Chen Xing Ma Jinhong Guo Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning Fundamental Research Upconverting nanoparticles Lateral flow assays Transfer learning Internet of medical things Portable fluorescent sensor |
| title | Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning |
| title_full | Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning |
| title_fullStr | Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning |
| title_full_unstemmed | Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning |
| title_short | Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning |
| title_sort | artificial intelligence reinforced upconversion nanoparticle based lateral flow assay via transfer learning |
| topic | Upconverting nanoparticles Lateral flow assays Transfer learning Internet of medical things Portable fluorescent sensor |
| url | http://www.sciencedirect.com/science/article/pii/S266732582200200X |
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