Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Zhang, Weifeng; Sun, Hong; He, Shipei; Chen, Xun; Yao, Lin; Zhou, Liqun; Wang, Yi; Wang, Pu; Hong, Wang

doi:10.3389/fmicb.2022.874966

Cited by 6 publications

(5 citation statements)

References 46 publications

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“…Here, we collected the spectra of the bacteria by Raman confocal microscopy. Previous studies demonstrated that Raman spectroscopy has the ability to achieve rapid identification of pathogenic bacteria using DL. , DL neural networks such as a LSTM and variational autoencoders have been developed to improve the accuracy of bacterial identification. We differentiated the six different bacteria and built the Raman database.…”

Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2024

Anal. Chem.

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Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback−Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA−SVM, SVM, UMAP−SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

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Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2024

Anal. Chem.

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“…Here we collected the spectra of bacteria by Raman confocal microscopy. Previous studies demonstrated that Raman spectroscopy has the ability to achieve rapid identification of pathogenic bacte-ria using deep learning 3,36 . Deep learning neural networks such as a long short-term memory (LSTM) 17 and Variational autoencoders (VAE) 37 have been developed to improve the accuracy of bacterial identification.…”

Section: Resultsmentioning

confidence: 99%

Applications of Data Characteristic AI-assisted Raman Spectroscopy in Pathological Classification

Chen,

Shen,

Liu

et al. 2023

Preprint

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Raman spectroscopy has been widely used for label-free biomolecular analysis of cell and tissue for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy including machine learning (PCA and SVM), manifold learning (UMAP) and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral datasets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and key Raman shifts, explore the performance of different AI models (e.g. PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). Tissue sites mean that spectral collection sites from sample, KL divergence means the divergence between spectra of different types. We found that for dataset of large spectral data size, Resnet performed better than PCA-SVM and UMAP, for dataset of small spectral data size, PCA-SVM or UMAP performed better. We also optimized the network parameters (e.g. principal components, activation function, and loss function) of AI model based on data characteristics. Using AI classification models, the mean area under receiver operating characteristic curves (AUC) for representative datasets reached 0.966, with mean sensitivity of 89.6%, mean specificity of 95.4%, mean accuracy of 93.4%, and mean time expense of 5 seconds. By using data characteristic assisted AI classification model, the accuracy improves from 85.1% to 94.6% for endometrial carcinoma grading, from 77.l% to 90.7% for hepatoma extracellular vesicles detection, from 89.3% to 99.7% for melanoma cell detection, from 88.1% to 97.9% for bacterial identification, from 53.7% to 85.5% for diabetic skin screening. Furthermore, according to the saliency maps, we found classification-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen), which contribute to the pathological diagnosis classification. Data characteristic assisted AI classification model was demonstrated to improve the robustness and accuracy of Raman spectroscopy in pathological classification. Collectively, this study opens up new opportunities for accurate and rapid Raman optical biopsy.

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“…The spectral features were distinct for each of the pathogenic bacteria and thus facilitated the identification [ 216 ]. Raman scattering microscopy was also useful for the rapid identification and AST of pathogens in urine [ 217 ] and notable in its ability to classify on a Gram-staining basis and AST results within ~3 h drawing attention for clinical applications.…”

Section: Proteomic Studies On Eskape Resistancementioning

confidence: 99%

Antibiotic Resistance Diagnosis in ESKAPE Pathogens—A Review on Proteomic Perspective

Kalpana

Lin²,

Wang

et al. 2023

Diagnostics

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Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the “imminent pandemic”.

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Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Cited by 6 publications

References 46 publications

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Applications of Data Characteristic AI-assisted Raman Spectroscopy in Pathological Classification

Antibiotic Resistance Diagnosis in ESKAPE Pathogens—A Review on Proteomic Perspective

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