Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Abstract

Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

“…Sh et al [ 129 ] extracted individualized blood messenger ribonucleic acid (mRNA) expression data from the GEO database. These data were analyzed with the xCell webtool, which can be found at https://comp health.ucsf.edu/app/xcell , to get blood cell composition and quantitative data.…”

A glance through the effects of CD4+ T cells, CD8+ T cells, and cytokines on Alzheimer's disease

“…The elevated levels of apoptosis in peripheral lymphocytes of AD patients are mostly caused by CD4 + cells, with no alterations in CD8 + T cells' apoptosis susceptibility found [172] . Another study observed a notable decrease in CD4 + and CD8 + T cells among individuals with AD [173] . CD8 + T cells had higher proportions in control samples than in AD samples, while CD4 + T cells had a higher proportion in the AD samples [174] .…”

“… AD patients vs control group rised ratio of CD4 + /CD8 + due to reduction of CD8 + T cells in AD Pirttilä et al 1992, [168] . AD group vs control group notable decrease in CD4 + and CD8 + T cells Sh et al 2021, [173] . AD patients mild AD: no differences in CD4 + T cells and CD8 + T cells moderately severe AD: a huge rise of CD4 + T cells, a slight reduction in CD8 + T cells Shalit et al 1995, [27] .…”

Active regression model for clinical grading of COVID-19

“…Deep learning-based artificial intelligence algorithms have recently demonstrated distinct advantages in the accurate detection and treatment of diseases ( 17 – 21 ). Using a multi-classification modeling approach with unique risk indices, this method simultaneously predicts numerous disease states and generates a model with a high level of accuracy ( 22 , 23 ). Using CT images of COVID-19 patients, researchers from the Macau University of Science and Technology presented their findings on deep learning modeling in the journal Cell in June of 2020 ( 24 ).…”