Detection of Alzheimer’s by Machine Learning-assisted Vibrational Spectroscopy in Human Cerebrospinal Fluid

Arévalo, Laura A.; Антонова, О. В.; O’Brien, Stephen A.; Singh, Gajendra; Seifert, Andreas

doi:10.1088/1742-6596/2407/1/012026

Cited by 2 publications

(2 citation statements)

References 23 publications

(25 reference statements)

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“…The LR model was used for the classification of new analogues of drugs at a high risk of being abused, belonging to the class of hallucinogenic amphetamines, based on their FTIR spectra [27]. L.A. Arevalo et al reported that a LR model can discriminate between healthy controls and Alzheimer's patients with a precision of 98% when the input for the model combines data from both Raman and FTIR spectra measured for cerebral spinal fluid [28].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm−1) and in the high-wavenumber range (2700–3700 cm−1). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

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Section: Introductionmentioning

confidence: 99%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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“…Teh et al achieved accuracies of 92% and 94% for tissue classification of gastric adenocarcinomas of intestinal and diffuse type, respectively, by the LR method applied to Raman spectra of such tissues [24]. More recently, L.A. Arevalo et al successfully discriminated vibrational spectra of cerebral-spinal fluid from healthy and Alzheimer's patients by the LR method [25].…”

Section: Introductionmentioning

confidence: 99%

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2024

Photonics

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Colorectal cancer is very widespread in developed countries. Its diagnosis partly depends on pathologists’ experience and their laboratories’ instrumentation, producing uncertainty in diagnosis. The use of spectroscopic techniques sensitive to the cellular biochemical environment could aid in achieving a reliable diagnosis. So, we used Raman micro-spectroscopy, combined with a spectral analysis by means of machine learning methods, to build classification models, which allow colon cancer to be diagnosed in cell samples, in order to support such methods as complementary tools for achieving a reliable identification of colon cancer. The Raman spectra were analyzed in the 980–1800 cm−1 range by focusing the laser beam onto the nuclei and the cytoplasm regions of single FHC and CaCo-2 cells (modelling healthy and cancerous samples, respectively) grown onto glass coverslips. The comparison of the Raman intensity of several spectral peaks and the Principal Component Analysis highlighted small biochemical differences between healthy and cancerous cells mainly due to the larger relative lipid content in the former cells with respect to the latter ones and to the larger relative amount of nucleic acid components in cancerous cells compared with healthy ones. We considered four classification algorithms (logistic regression, support vector machine, k nearest neighbors, and a neural network) to associate unknown Raman spectra with the cell type to which they belong. The built machine learning methods achieved median values of classification accuracy ranging from 95.5% to 97.1%, sensitivity values ranging from 95.5% to 100%, and specificity values ranging from 93.9% to 97.1%. The same median values of the classification parameters, which were estimated for a testing set including unknown spectra, ranged between 93.1% and 100% for accuracy and between 92.9% and 100% for sensitivity and specificity. A comparison of the four methods pointed out that k nearest neighbors and neural networks better perform the classification of nucleus and cytoplasm spectra, respectively. These findings are a further step towards the perspective of clinical translation of the Raman technique assisted by multivariate analysis as a support method to the standard cytological and immunohistochemical methods for diagnostic purposes.

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Detection of Alzheimer’s by Machine Learning-assisted Vibrational Spectroscopy in Human Cerebrospinal Fluid

Cited by 2 publications

References 23 publications

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

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