Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms

Chen, Cheng; Yang, Li; Zhao, Jianyong; Yuan, Yushuai; Chen, Chen; Tang, Jun; Yang, Hong; Yan, Ziwei; Wang, Hang; Lv, Xiaoyi

doi:10.1016/j.ijleo.2019.164043

Cited by 33 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, prominent peaks of urea (587, 1006 and 1170 cm

), creatinine (670 and 1423 cm

), CH

bending (1301 cm

), hydroxybutyrate (1456 cm

) and uric acid (1423, 1595 and 1650 cm

) were seen in the non-AKI urine samples. There was also a sharp peak of porphyrin (1621 cm

) in the AKI urine samples, which was possibly due to the effect of a metabolic disorder that causes an enzyme deficiency [ 24 ]. The nitrogenous compounds, which were mostly observed as ethanolamine bands at 880 and 1079 cm

, were most likely created by the urea cycle’s amino acid metabolism, and higher concentrations were found in the urine of chronic kidney disease patients [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury

Jeng

Sharma

Lee

et al. 2022

JCM

View full text Add to dashboard Cite

Acute kidney injury (AKI) is a common syndrome characterized by various etiologies and pathophysiologic processes that deteriorate kidney function. The aim of this study is to identify potential biomarkers in the urine of non-acute kidney injury (non-AKI) and AKI patients through Raman spectroscopy (RS) to predict the advancement in complications and kidney failure. Selected spectral regions containing prominent peaks of renal biomarkers were subjected to partial least squares linear discriminant analysis (PLS-LDA). This discriminant analysis classified the AKI patients from non-AKI subjects with a sensitivity and specificity of 97% and 100%, respectively. In this study, the RS measurements of urine specimens demonstrated that AKI had significantly higher nitrogenous compounds, porphyrin, tryptophan and neopterin when compared with non-AKI. This study’s specific spectral information can be used to design an in vivo RS approach for the detection of AKI diseases.

show abstract

“…However, prominent peaks of urea (587, 1006 and 1170 cm

), creatinine (670 and 1423 cm

), CH

bending (1301 cm

), hydroxybutyrate (1456 cm

) and uric acid (1423, 1595 and 1650 cm

) were seen in the non-AKI urine samples. There was also a sharp peak of porphyrin (1621 cm

, were most likely created by the urea cycle’s amino acid metabolism, and higher concentrations were found in the urine of chronic kidney disease patients [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury

Jeng

Sharma

Lee

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Raman spectroscopy of urine samples can diagnose chronic renal failure (CRF) disease with an acceptable accuracy, as shown in the work of Chen et al [149], in which genetic algorithms aided in RS obtained accuracies of between approximately 65% and 85%.…”

Section: Biological Applicationsmentioning

confidence: 99%

A Comprehensive Review on Raman Spectroscopy Applications

et al. 2021

View full text Add to dashboard Cite

Raman spectroscopy is a very powerful tool for material analysis, allowing for exploring the properties of a wide range of different materials. Since its discovery, Raman spectroscopy has been used to investigate several features of materials such carbonaceous and inorganic properties, providing useful information on their phases, functions, and defects. Furthermore, techniques such as surface and tip enhanced Raman spectroscopy have extended the field of application of Raman analysis to biological and analytical fields. Additionally, the robustness and versatility of Raman instrumentations represent a promising solution for performing on-field analysis for a wide range of materials. Recognizing the many hot applications of Raman spectroscopy, we herein overview the main and more recent applications for the investigation of a wide range of materials, such as carbonaceous and biological materials. We also provide a brief but exhaustive theoretical background of Raman spectroscopy, also providing deep insight into the analytical achievements.

show abstract

“…With the rapid development of artificial intelligence technology in the medical field 10 – 12 more and more CAD systems, especially convolutional neural network (CNN)-based CAD systems, are applied to automatic analysis tasks of histopathological images, such as cell nucleus detection and classification 13 , tumor segmentation 14 , tumor metastasis detection 15 , 16 , and cancer grading 17 . However, when faced with smaller medical image datasets, CNN models often fail to extract effective information from the dataset.…”

Section: Introductionmentioning

confidence: 99%

HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism

Zhou

Cao

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non_Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer.

show abstract

Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms

Cited by 33 publications

References 25 publications

Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury

Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury

A Comprehensive Review on Raman Spectroscopy Applications

HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism

Contact Info

Product

Resources

About