A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier

Cheng, Ningtao; Chen, Dajing; Liu, Bin; Fu, Jing; Wang, Hongyang

doi:10.1016/j.bios.2021.113246

Cited by 45 publications

(31 citation statements)

References 35 publications

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Section: Resultsmentioning

confidence: 99%

“…CNN-based deep-learning algorithms are advanced artificial-intelligence systems with good performance in recognizing images or spectral data for disease detection [ 41 , 42 ]. For a Raman spectral analysis, CNN algorithms have been used to successfully distinguish liver cancer [ 41 ], prostate cancer [ 43 ], and breast cancer [ 44 ] from control groups. However, these studies only focused on cancer screening and did not consider clinical staging (multi-classification detection), which is closely correlated with the survival rates of cancer patients.…”

Section: Resultsmentioning

confidence: 99%

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Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

et al. 2022

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Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

et al. 2022

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“…Moreover, the intensity of Raman peak at 1152 and 1514cm -1 assigned for carotenoids is also decreased. (15,26). This may be because deep learning has better learning effects on high-dimensional complex data, such as highdimension images with semantic information.…”

Section: Discussionmentioning

confidence: 99%

A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

Huang

et al. 2021

Front. Oncol.

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Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

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“…For example, a convolutional neural network (CNN), which is one of the most popular deep learning architectures, has been widely used and has shown superior performance in analyzing spectroscopic signals including those from SERS spectroscopy of complex biological samples. [23][24][25] In this paper, the SERS spectra of eleven bacterial endotoxins have been measured based on silver nanorod array (AgNR) substrates. The characteristic SERS peaks from these endotoxins have been identified.…”

Section: Introductionmentioning

confidence: 99%

Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

Yang

Haverstick

et al. 2022

Nanoscale

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A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier

Cited by 45 publications

References 35 publications

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

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