A machine-learning strategy to evaluate the use of FTIR spectra of saliva for a good control of type 2 diabetes

Sánchez-Brito, Miguel; Luna-Rosas, Francisco Javier; Mendoza-González, Ricardo; Mata-Miranda, Mónica Maribel; Romo, Julio César Martínez; Vázquez-Zapién, Gustavo Jesús

doi:10.1016/j.talanta.2020.121650

Cited by 16 publications

(8 citation statements)

References 5 publications

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“…Likewise, the analysis by intensities combined with a classification model such as SVMs makes it possible to identify with high specificity when periodontitis is active in patients. SVMs have proven to be an efficient tool in the study of saliva Raman spectra, as evidenced by Radzol et al [ 66 ] and Sánchez‐Brito et al, [ 67 ] who evaluated the responsiveness of an SVM in the identification of a salivary biomarker. The above results indicate that the proposed biomarkers, together with Raman spectroscopy, would critically define the progression of the disease using a multiclass supervised learning strategy.…”

Section: Discussionmentioning

confidence: 99%

Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

Villalba-Hernández

Moyaho-Bernal

Narea-Jiménez

et al. 2022

J Raman Spectroscopy

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Many research areas have developed techniques to diagnose lung cancer, cardiovascular diseases, stress, caries, and periodontitis by analyzing saliva. This paper describes a study that predicts periodontitis based on Raman spectra of saliva and biomarkers, such as albumin and alanine aminotransferase (ALT). The spectra were smoothed using a Whittaker filter and baseline correction in MATLAB. In addition, a residual analysis of intensities was performed, and the root mean square deviation was calculated and used as a threshold to establish the active bands of interest, based on the Raman bands associated with albumin and ALT. ORCA quantum chemistry software was used to predict the fundamental frequencies and intensities of some saliva constituents. Support vector machines were used to perform spectral distinction and discriminate between healthy and periodontitis patients.

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Section: Discussionmentioning

confidence: 99%

Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

Villalba-Hernández

Moyaho-Bernal

Narea-Jiménez

et al. 2022

J Raman Spectroscopy

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“…Nonlinear and linear predictive algorithms [152] 2787 consecutive participants Combination of elastic network with RF, SVM, and back-propagation artificial neural network (BP-ANN) algorithms as well as LR [155] 1772 paired data varying from 65 ~ 492 mg/dl and GBT [133] Insulin resistance predicting models 8842 Koreans participants LR, XGBoost, random forest, and ANN [159] 1344 samples HOMA-IR model [160] 2433 T2DM patients MIL-Boost [161] 968 patients not affected by T2DM (FIMMG_obs dataset)…”

Section: Healthy Subjectsmentioning

confidence: 99%

“…In addition, about 98.4% of the predicted BG amounts were inside area A of the Clarke error grid. The Fourier-transform infrared spectra data of saliva was modeled by SVM, ANN, and LR to reach the following purposes: characterization of diabetic patients in uncontrolled and controlled based on their recorded preprandial HbA1C amounts, characterization of diabetic cases according to their pre-prandial glucose amounts obtained at the time of taking the saliva sample, and assessment of a specific glucose amount [152]. The outcomes revealed that the abovementioned examinations are possible through ANN using regression models.…”

Section: Detection Of Blood Glucosementioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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“…Recently, FTIR has been applied to study saliva from diabetic patients [ 16 , 17 , 18 , 19 , 20 , 21 ] and patients with oral pathologies [ 22 , 23 ] and to identify cancer biomarkers [ 4 , 24 , 25 ] and COVID-19-related biomarkers [ 26 , 27 , 28 , 29 ]. Recently, ATR-FTIR spectra in tandem with chemometric have been employed to analyze the spectral changes in semen, saliva, and urine in violent crimes during dry out, allowing to estimate their time since deposition [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

et al. 2023

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Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.

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A machine-learning strategy to evaluate the use of FTIR spectra of saliva for a good control of type 2 diabetes

Cited by 16 publications

References 5 publications

Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

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