Machine learning empowered coherent Raman imaging and analysis for biomedical applications
Abstract In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delive...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00345-1 |
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| _version_ | 1832585699464839168 |
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| author | Yihui Zhou Xiaobin Tang Delong Zhang Hyeon Jeong Lee |
| author_facet | Yihui Zhou Xiaobin Tang Delong Zhang Hyeon Jeong Lee |
| author_sort | Yihui Zhou |
| collection | DOAJ |
| description | Abstract In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques. |
| format | Article |
| id | doaj-art-3b87d43245724066893a220dc03d9b07 |
| institution | Kabale University |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-3b87d43245724066893a220dc03d9b072025-01-26T12:36:03ZengNature PortfolioCommunications Engineering2731-33952025-01-014111410.1038/s44172-025-00345-1Machine learning empowered coherent Raman imaging and analysis for biomedical applicationsYihui Zhou0Xiaobin Tang1Delong Zhang2Hyeon Jeong Lee3College of Biomedical Engineering & Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang UniversityInterdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang UniversityInterdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang UniversityCollege of Biomedical Engineering & Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang UniversityAbstract In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.https://doi.org/10.1038/s44172-025-00345-1 |
| spellingShingle | Yihui Zhou Xiaobin Tang Delong Zhang Hyeon Jeong Lee Machine learning empowered coherent Raman imaging and analysis for biomedical applications Communications Engineering |
| title | Machine learning empowered coherent Raman imaging and analysis for biomedical applications |
| title_full | Machine learning empowered coherent Raman imaging and analysis for biomedical applications |
| title_fullStr | Machine learning empowered coherent Raman imaging and analysis for biomedical applications |
| title_full_unstemmed | Machine learning empowered coherent Raman imaging and analysis for biomedical applications |
| title_short | Machine learning empowered coherent Raman imaging and analysis for biomedical applications |
| title_sort | machine learning empowered coherent raman imaging and analysis for biomedical applications |
| url | https://doi.org/10.1038/s44172-025-00345-1 |
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