Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network

Wang, Xin; Tian, Shengwei; Yu, Long; Lv, Xiaoyi; Zhang, Zhaoxia

doi:10.1007/s10103-020-03003-4

Cited by 21 publications

(12 citation statements)

References 36 publications

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“…The accuracy rate was 96.15%, higher than the 91.53% of traditional NN. Wang et al [118] also identified patients with type B pneumonia from Raman spectroscopy, used PCA to reduce dimensionality, and then used LSTM to process long-span data, finally reaching an accuracy of 97.32%. Both papers use PCA for dimensionality reduction.…”

Section: Hepatitismentioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

163

104

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In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.

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Section: Hepatitismentioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

163

104

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“…RNNs have been employed to clas- sify the whole-slide images of adenocarcinoma, adenoma, and non-neoplastic histopathology on biopsy, 27 and to discriminate the Raman spectra of hepatitis B serum with high accuracy and sensitivity. 28 The Raman fingerprint region (633-1723 cm −1 ) of the frozen and FFPE samples was used for developing the LSTM model. We have performed a grid search over the hyperparameter space.…”

Section: Establishment Of the Deep Learning Algorithms By Recurrent N...mentioning

confidence: 99%

“…In addition, Raman shifts of 856 cm −1 , 939 cm −1 , 1004 cm −1 , 1245 cm −1 , 1451 cm −1 , and 1666 cm −1 found in skeletal muscle represented collagen 31,43 and amide III; 1083 cm −1 , 1303 cm −1 , 1441 cm −1 , and 1657 cm −1 in adipose tissues were lipid 44 and fatty acid assignments. 36 Furthermore, prominent Raman shifts found in blood cells represented hemoglobin molecules (677 cm −1 , 755 cm −1 , 827 cm −1 , and 1224 cm −1 ) 45,46 and protein vibrational modes (622 cm −1 , 1003 cm −1 , and 1342 cm −1 ) 27,28,33 Table 2 Putative assignments of significant Raman shifts identified in FFPE normal (18) and tumor (42) specimens. The Raman spectral dataset was analyzed by unpaired t test and P values were adjusted with the Bonferroni-Dunn method for multiple comparisons (*P < 0.05)…”

Section: Raman Signatures Of the Deparaffinized Tissue Sections Of Th...mentioning

confidence: 99%

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Lau

Law³

et al. 2022

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“…Several diagnostic applications have been reported: [ 7 , 8 ], also in the context of cancer: [ 9 – 12 ], and in obesity-related diseases: [ 13 – 15 ]. Other studies deal with specific applications using blood: [ 16 – 19 ], or molecular imaging: [ 20 , 21 ]. Theoretically, due to the unique signatures of substances, countless biomarkers and metabolites could be analyzed in parallel within about one minute, enabling a fast medical response.…”

Section: Introductionmentioning

confidence: 99%

Raman spectroscopy on blood serum samples of patients with end-stage liver disease

et al. 2021

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Raman spectroscopy has shown to be a promising method for the examination of biomedical samples. However, until now, its efficacy has not been established in clinical diagnostics. In this study, Raman spectroscopy’s potential application in medical laboratories is evaluated for a large variety (38) of biomarkers. Given 234 serum samples from a cohort of patients with different stages of liver disease, we performed Raman spectroscopy at 780nm excitation wavelength. The Raman spectra were analyzed in combination with the results of routine diagnostics using specifically developed complex mathematical algorithms, including fluorescence filtering, frequency subset selection and several overfitting circumventing strategies, such as independent validation. With the results of this cohort, which were validated in 328 independent samples, a significant proof-of-concept study was completed. This study highlights the need to prevent overfitting and to use independent data for validation. The results reveal that Raman spectroscopy has high potential for use in medical laboratory diagnostics to simultaneously quantify multiple biomarkers.

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Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network

Cited by 21 publications

References 36 publications

Multi-Disease Prediction Based on Deep Learning: A Survey

Multi-Disease Prediction Based on Deep Learning: A Survey

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Raman spectroscopy on blood serum samples of patients with end-stage liver disease

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