Abstract
This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B.
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Funding
This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61765014); Reserve Talents Project of National High-level Personnel of Special Support Program (QN2016YX0324); Urumqi Science and Technology Project (No.P161310002 and Y161010025); and Reserve Talents Project of National High-level Personnel of Special Support Program (Xinjiang [2014]22).
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Wang, X., Tian, S., Yu, L. et al. Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network. Lasers Med Sci 35, 1791–1799 (2020). https://doi.org/10.1007/s10103-020-03003-4
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DOI: https://doi.org/10.1007/s10103-020-03003-4