Raman spectral post-processing for oral tissue discrimination – a step for an automatized diagnostic system

Abstract

Most oral injuries are diagnosed by histopathological analysis of a biopsy, which is an invasive procedure and does not give immediate results. On the other hand, Raman spectroscopy is a real time and minimally invasive analytical tool with potential for the diagnosis of diseases. The potential for diagnostics can be improved by data post-processing. Hence, this study aims to evaluate the performance of preprocessing steps and multivariate analysis methods for the classification of normal tissues and pathological oral lesion spectra. A total of 80 spectra acquired from normal and abnormal tissues using optical fiber Raman-based spectroscopy (OFRS) were subjected to PCA preprocessing in the z-scored data set, and the KNN (K-nearest neighbors), J48 (unpruned C4.5 decision tree), RBF (radial basis function), RF (random forest), and MLP (multilayer perceptron) classifiers at WEKA software (Waikato environment for knowledge analysis), after area normalization or maximum intensity normalization. Our results suggest the best classification was achieved by using maximum intensity normalization followed by MLP. Based on these results, software for automated analysis can be generated and validated using larger data sets. This would aid quick comprehension of spectroscopic data and easy diagnosis by medical practitioners in clinical settings.

Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

“…Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy associated with machine learning methods could be a potential alternative method for diagnosis of COVID-19, as it is simple, label-free and cost-effective. This technique has shown promise as a diagnostic or screening tool in several diseases such as cancer 12 – 14 , diabetes, hypertension, and physiological stress 15 , 16 . In 2018, Leal et al 15 and Baker et al 17 pointed out that small samples of biofluids (saliva, blood and urine) could be effective for the diagnosis of a wide range of diseases, including infectious diseases 18 .…”

Intestinal anastomosis automation through diffuse reflectance spectroscopy: towards real time guidance during robotic surgery

“…When suturing millimeter-size structures such as tissues layers to be matched during intestinal anastomosis, conventional medical technologies based on tissue structural parameters have limited precision. Instead, molecular-sensitive technologies such as optical spectroscopy [17][18][19][20][21][22][23][24][25][26][27][28][29][30] can identify tissues with high accuracy, in addition to their potential to be miniaturized and integrated into medical devices. A promising technology is reflectance spectroscopy 29,[34][35][36][37][38][39][40] .…”

Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization

“…Therefore, scattering is associated with the tissue microstructure. On the other hand, absorption is associated with the tissue biomolecular composition, as its absorption bands are dependent on the molecular energy levels (including electronic [ 10 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], vibrational [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ] and rotational levels [ 57 ]). Tissue absorption is related to biological chromophores (or absorbing biomolecules), which were previously investigated in a number of clinical and preclinical studies [ 58 ].…”