Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

Korb, Elke; Bağcıoğlu, Murat; Garner-Spitzer, Erika; Wiedermann, Ursula; Ehling‐Schulz, Monika; Schabussova, Irma

doi:10.3390/biom10071058

Cited by 13 publications

(12 citation statements)

References 72 publications

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“…30,79,81–84 and can result in better classification models afterwards. 72,85–93 Since derivation enhances the noise level of spectra, it is only advisable to use on high S/N data.…”

Section: Resultsmentioning

confidence: 99%

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

et al. 2021

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The chemical composition of recent and fossil organic-walled dinoflagellate cyst walls and its diversity is poorly understood and analyses on single microscopic specimens are rare. A series of infrared spectroscopic experiments resulted in the proposition of a standardized attenuated total reflection micro-Fourier transform infrared-based method that allows the collection of robust data sets consisting of spectra from individual dinocysts. These data sets are largely devoid of nonchemical artifacts inherent to other infrared spectrochemical methods, which have typically been used to study similar specimens in the past. The influence of sample preparation, specimen morphology and size and spectral data processing steps is also assessed within this methodological framework. As a result, several guidelines are proposed which facilitate the collection and qualitative interpretation of highly reproducible and repeatable spectrochemical data. These, in turn, pave the way for a systematic exploration of dinocyst chemistry and its assessment as a chemotaxonomical tool or proxy.

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“…30,79,81–84 and can result in better classification models afterwards. 72,85–93 Since derivation enhances the noise level of spectra, it is only advisable to use on high S/N data.…”

Section: Resultsmentioning

confidence: 99%

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

et al. 2021

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“…Their classification models of the FTIR spectra of human sera generated using deep learning showed an overall accuracy of 93.9%. Furthermore, the model they established successfully discriminated against allergy, allergen-specific immunotherapy, and healthy individuals with true positive rates of 93.3, 91.7, and 96.7%, respectively [44]. Another FTIR study classifying inflammatory fibrous hyperplasia (IFH) lesions and normal oral mucosa (NM) using the PCA-LDA approach had a sensitivity and specificity of 87.5 and 100%, respectively [69].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the application of ATR-FTIR combined with a neural network could differentiate between sera from healthy subjects and from breast cancer patients with a sensitivity of 92-95% and specificity of 95-100% [43]. Another finding reported the possibility of ATR-FTIR coupled with deep learning to be applied for stratifying healthy, allergic, and allergen-specific immuno-therapy in mice and humans [44]. Unfortunately, to the best of our knowledge, ATR-FTIR empowered with machine learning algorithms has not been widely applied to investigate immunosenescence.…”

Section: Introductionmentioning

confidence: 98%

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

Praja

Wongwattanakul

Tippayawat

et al. 2022

Cells

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In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700–1500 cm−1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700–1500 cm−1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800–900 cm−1. Exosome spectra in the 1700–1500 and combined 3000–2800 and 1800–900 cm−1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800–900 cm−1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

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“…In addition, some researchers have used a combination of FTIR spectroscopy and ML to propose a new strategy for canine visceral leishmaniasis diagnosis . Similarly, researchers demonstrated the potential of this approach to quickly diagnose clinical therapeutic monitoring in patients with allergies . Many studies have shown that using FTIR spectroscopy combined with ML has great clinical potential. − …”

mentioning

confidence: 99%

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Wang

et al. 2022

J. Phys. Chem. Lett.

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Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

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Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

Cited by 13 publications

References 72 publications

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

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