Three-step one-way model in terahertz biomedical detection
Abstract Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Her...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Singapore
2021
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| Online Access: | https://hdl.handle.net/1721.1/136927 |
| _version_ | 1826209349424381952 |
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| author | Peng, Yan Huang, Jieli Luo, Jie Yang, Zhangfan Wang, Liping Wu, Xu Zang, Xiaofei Yu, Chen Gu, Min Hu, Qing Zhang, Xicheng Zhu, Yiming Zhuang, Songlin |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Peng, Yan Huang, Jieli Luo, Jie Yang, Zhangfan Wang, Liping Wu, Xu Zang, Xiaofei Yu, Chen Gu, Min Hu, Qing Zhang, Xicheng Zhu, Yiming Zhuang, Songlin |
| author_sort | Peng, Yan |
| collection | MIT |
| description | Abstract
Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases. |
| first_indexed | 2024-09-23T14:21:08Z |
| format | Article |
| id | mit-1721.1/136927 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:21:08Z |
| publishDate | 2021 |
| publisher | Springer Singapore |
| record_format | dspace |
| spelling | mit-1721.1/1369272023-09-19T18:14:41Z Three-step one-way model in terahertz biomedical detection Peng, Yan Huang, Jieli Luo, Jie Yang, Zhangfan Wang, Liping Wu, Xu Zang, Xiaofei Yu, Chen Gu, Min Hu, Qing Zhang, Xicheng Zhu, Yiming Zhuang, Songlin Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics Abstract Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases. 2021-11-01T14:34:15Z 2021-11-01T14:34:15Z 2021-07-23 2021-07-25T03:20:48Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136927 PhotoniX. 2021 Jul 23;2(1):12 PUBLISHER_CC en https://doi.org/10.1186/s43074-021-00034-0 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Singapore Springer Singapore |
| spellingShingle | Peng, Yan Huang, Jieli Luo, Jie Yang, Zhangfan Wang, Liping Wu, Xu Zang, Xiaofei Yu, Chen Gu, Min Hu, Qing Zhang, Xicheng Zhu, Yiming Zhuang, Songlin Three-step one-way model in terahertz biomedical detection |
| title | Three-step one-way model in terahertz biomedical detection |
| title_full | Three-step one-way model in terahertz biomedical detection |
| title_fullStr | Three-step one-way model in terahertz biomedical detection |
| title_full_unstemmed | Three-step one-way model in terahertz biomedical detection |
| title_short | Three-step one-way model in terahertz biomedical detection |
| title_sort | three step one way model in terahertz biomedical detection |
| url | https://hdl.handle.net/1721.1/136927 |
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