Accurate Diagnosis Of Alzheimer's Disease Research Articles

Resolution of the Expert Council on the problem of early diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and the most common cause of dementia. In daily practice, AD is often diagnosed late, while the early stages of the disease are overlooked or mistaken for cerebrovascular pathology. However, the efficacy of existing and newly developed (disease-modifying) AD therapies is the greatest in the early stages of the disease. An accurate diagnosis of AD is possible when biological markers of the main pathological process (cerebral amyloidosis, tauopathy) are detected using positron emission tomography or neurochemical examination of cerebrospinal fluid, which are gradually being introduced into practice in Russia. The experts discussed the clinical aspects of the use of biological markers, obtained in the leading specialized centers of our country for the diagnosis and treatment of cognitive impairment (CI). First and foremost, biomarker testing is indicated in patients with mild CI and mild dementia possibly associated with AD, so that disease-modifying (pathogenetic) therapy can be initiated as early as possible upon on its availability (currently, drugs for anti-amyloid disease-modifying therapy are not registered in the Russian Federation). Patients with a non-classical (non-amnestic) or atypical AD phenotype are another group of patients in whom it is also advisable to analyze biomarkers for differential diagnostic purposes.

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.

The dilemma and comprehensive assessment for a precise diagnosis of Alzheimer's disease

The clinical and neuroimaging findings of Alzheimer's disease (AD) are significantly heterogeneous. There are uncertaintie in the current widely available clinical and imaging diagnosis of AD. In the era of amyloid-targeted monoclonal antibody therapy, accurate diagnosis of Alzheimer's disease is urgently needed. Positron emission tomography (PET) and cerebrospinal fluid (CSF) examination can effectively reflect the β-amyloid and neurofibrillary tangles pathology in AD, thus effectively improving the accuracy of AD diagnosis. Understanding the value of various CSF and PET biomarkers in diagnosing/staging AD is a prerequisite for clinical application of biomarkers. The selection and interpretation of biomarkers should fully consider the clinical features and structural imaging of dementia patients, while paying attention to the degree of concordance between the clinical stages and the biological stages reflected by the biomarkers. Thus, biomarkers are expected to play more additional value in AD diagnosis and treatment.

Prediction of Alzheimer’s Disease with Retinal Images Using Deep Learning

Alzheimer's disease (AD) is a leading cause of mortality worldwide. Early detection and accurate diagnosis of AD are crucial for effective intervention and improved patient outcomes. Retinal imaging has emerged as a non-invasive and cost-effective technique for AD prediction. This study aims to develop a deep learning model using convolutional neural networks (CNNs) architecture to predict AD from retinal images. The proposed model leverages the capabilities of CNNs to automatically learn relevant features from retinal images.

Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer's Disease.

An improved understanding of the pathobiology of Alzheimer's disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer's disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer's disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.

A Hybrid Deep Learning Approach for Accurate Alzheimer's Disease Diagnosis Using MRI Data

A Hybrid Deep Learning Approach for Accurate Alzheimer's Disease Diagnosis Using MRI Data

Self-calibrating surface-enhanced Raman scattering-lateral flow immunoassay for determination of amyloid-β biomarker of Alzheimer's disease

Rapid early diagnosis of Alzheimer's disease (AD) is critical for its effective and prompt treatment since the clinically available treatments can only relieve the symptoms or slow the disease progression. However, it is still a grand challenge to accurately diagnose AD at its early stage because of the indiscernible early symptoms and the lack of sensitive detection tools. Here, we develop a self-calibrating surface-enhanced Raman scattering (SERS)-lateral flow immunoassay (LFIA) biosensor for quantitative analysis of amyloid-β1-42 (Aβ1-42) biomarker in biofluids, enabling accurate AD diagnosis. The designed SERS-LFIA biosensor makes full use of the unique aspects of the LFIA format and the SERS technique to quantify the Aβ1-42 level in complex biofluids with high sensitivity, excellent anti-interference capability, low-cost, and operation simplicity. The key aspect of the design of this biosensor is that internal standard (IS)-SERS nanoparticles are embedded in the test line of the test strip as a self-calibration unit for correction of fluctuations of SERS signals caused by various external factors such as test parameters and sample heterogeneity. We demonstrate significant improvement of the detection performance of the SERS-LFIA biosensor for ratiometric quantification of Aβ1-42 owing to the built-in IS in the test line. We expect that the present IS-based biosensing strategy provides a promising tool for accurate AD diagnosis and longitudinal monitoring of therapeutic response with great promises for clinical translation.

Comparative Analysis of Deep Learning Models for Multiclass Alzheimer’s Disease Classification

INTRODUCTION: The terrible neurological condition is known Worldwide; millions of individuals are affected with Alzheimer's disease (AD). Effective treatment and management of AD depend on early detection and a precise diagnosis. An effective method for identifying anatomical and functional abnormalities in the brain linked to AD is magnetic resonance imaging (MRI).
 OBJECTIVES: However, manual MRI scan interpretation requires a lot of time and is inconsistent between observers. The automated analysis of MRI images for AD identification and diagnosis using deep learning techniques has shown promise.
 METHODS: In this paper, we present a convolutional neural network (CNN)-based deep learning model for automatically classifying MRI images for Alzheimer's (AD) and a healthy control group. A huge dataset of MRI scans was used to train the CNN, which distinguished between AD and healthy control groups with excellent accuracy.
 RESULTS: Additionally, we looked into how transfer learning may be used to enhance pre-trained models and boost CNN performance. We discovered that transfer learning considerably increased the model's accuracy and decreased overfitting. Our findings show that MRI scans may be used to precisely detect and diagnose AD utilizing approaches to deep learning and machine learning.
 CONCLUSION: These techniques may improve the efficiency and accuracy of AD diagnosis and enable early disease identification, resulting in better AD management and therapy.

Improved Neural Network-Based System for Early and Accurate Diagnosis of Alzheimer Disease

Alzheimer's disorder is a neurological condition that develops over time and mainly impacts cognitive processes like memory, thought, and behavior. It is one of the most typical reasons for dementia, a syndrome marked by a loss of cognitive ability that interferes with individual daily activities. Recent techniques for diagnosing Alzheimer's illness frequently combine positron emission tomography (PET) scans with magnetic resonance imaging (MRI), which can identify mutations in the brain caused by the illness, such as the buildup of beta-amyloid plaques and tau tangles. Furthermore, analysis of blood samples and cerebrospinal fluid is also a widely used method for the diagnosis of Alzheimer’s disease. Machine learning and deep learning-based techniques play a vital role in examining complex structures in brain images and other data, contributing to the timely and precise identification of Alzheimer's disease. Artificial intelligence-based techniques can help prompt detection and treatment, leading to more efficient care for Alzheimer's disease. This study uses convolutional neural networks (CNN) with MRI-based datasets for early and accurate diagnosis of Alzheimer’s disease. The proposed approach has shown excellent results in AD diagnosis.

Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

Unveiling the Molecular Footprint: Proteome-Based Biomarkers for Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by progressive cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for implementing timely interventions and developing effective therapeutic strategies. Proteome-based biomarkers have emerged as promising tools for AD diagnosis and prognosis due to their ability to reflect disease-specific molecular alterations. There is of great significance for biomarkers in AD diagnosis and management. It emphasizes the limitations of existing diagnostic approaches and the need for reliable and accessible biomarkers. Proteomics, a field that comprehensively analyzes the entire protein complement of cells, tissues, or bio fluids, is presented as a powerful tool for identifying AD biomarkers. There is a diverse range of proteomic approaches employed in AD research, including mass spectrometry, two-dimensional gel electrophoresis, and protein microarrays. The challenges associated with identifying reliable biomarkers, such as sample heterogeneity and the dynamic nature of the disease. There are well-known proteins implicated in AD pathogenesis, such as amyloid-beta peptides, tau protein, Apo lipoprotein E, and clusterin, as well as inflammatory markers and complement proteins. Validation and clinical utility of proteome-based biomarkers are addressing the challenges involved in validation studies and the diagnostic accuracy of these biomarkers. There is great potential in monitoring disease progression and response to treatment, thereby aiding in personalized medicine approaches for AD patients. There is a great role for bioinformatics and data analysis in proteomics for AD biomarker research and the importance of data preprocessing, statistical analysis, pathway analysis, and integration of multi-omics data for a comprehensive understanding of AD pathophysiology. In conclusion, proteome-based biomarkers hold great promise in the field of AD research. They provide valuable insights into disease mechanisms, aid in early diagnosis, and facilitate personalized treatment strategies. However, further research and validation studies are necessary to harness the full potential of proteome-based biomarkers in clinical practice.

Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs.

Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.

Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework.

The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline. Based on the availability of plasma ATN biomarkers (plasma-derived Aβ42/40 , p-tau181, NFL, respectively), CSF ATN biomarkers (CSF-derived Aβ42 /Aβ40 , p-tau181, NFL), and neuroimaging ATN biomarkers (18F-florbetapir (FBP) amyloid-PET, 18F-flortaucipir (FTP) tau-PET, and fluorodeoxyglucose (FDG)-PET), a total of 2340 participants were selected from ADNI. Our data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N. Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.

Multimodal predictive classification of Alzheimer's disease based on attention‐combined fusion network: Integrated neuroimaging modalities and medical examination data

AbstractEarly diagnosis of Alzheimer's disease (AD) plays a key role in preventing and responding to this neurodegenerative disease. It has shown that, compared with a single imaging modality‐based classification of AD, synergy exploration among multimodal neuroimages is beneficial for the pathological identification. However, effectively exploiting multimodal information is still a big challenge due to the lack of efficient fusion methods. Herein, a multimodal fusion network based on attention mechanism is proposed, in which magnetic resonance imaging (MRI) and positron emission computed tomography (PET) images are converted into feature vectors with the same dimension, while the demographic information and clinical data are preprocessed and converted into feature vectors through embedding. This attention model can focus on important feature points, fuse the multimodal information more effectively, and thus provide accurate diagnosis and prediction for different pathological stages. The results show that the model achieves an accuracy of 84.1% for triple classification tasks in normal cognition (NC) versus mild cognitive impairment (MCI) versus AD and 93.9% prediction accuracy in stable MCI (sMCI) versus progressive MCI (pMCI). In contrast to the existing multimodal diagnosis methods, our model yields a state‐of‐the‐art accuracy of AD diagnosis, which is powerful and promising in clinical practice.

A Novel Serum-Based Diagnosis of Alzheimer's Disease Using an Advanced Phage-Based Biochip.

55 million people worldwide suffer from Alzheimer's disease (AD). A definitive diagnosis of AD is made postmortem after a neuropathological examination of the brain. There is an urgent need for an innovative, noninvasive methodology that allows for an early and reliable diagnosis. Several engineered phages that recognized Aβ-autoantibodies present in the sera of AD patients are previously identified. Here, novel phages are tested for their ability to accurately discriminate AD sera using immunophage-polymerase chain reaction in a miniatured biochip. It is found that five of the six phages analyzed discriminate between healthy controls and AD patients. Further, by combining the response of two phages, non-AD and severe AD cases are identified with 100% accuracy and mild-to-moderate cases with 90% accuracy. While the number of cases used here are relatively small and can be confirmed in larger cohorts, this first-of-a-kind system represents an innovative methodology with the potential of having a major impact in the AD field: from a clinical perspective, it can aid physicians in making an accurate AD diagnosis; from a research perspective, it can be used as a surrogate for AD clinical trials.

Precise profiling of exosomal biomarkers via programmable curved plasmonic nanoarchitecture-based biosensor for clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease of complex pathogenesis, with overt symptoms following disease progression. Early AD diagnosis is challenging due to the lack of robust biomarkers and limited patient access to diagnostics via neuroimaging and cerebrospinal fluid (CSF) tests. Exosomes present in body fluids are attracting attention as diagnostic biomarkers that directly reflect neuropathological features within the brain. In particular, exosomal miRNAs (exomiRs) signatures are involved in AD pathogenesis, showing a different expression between patients and the healthy controls (HCs). However, low yield and high homologous nature impede the accuracy and reproducibility of exosome blood-based AD diagnostics. Here, we developed a programmable curved plasmonic nanoarchitecture-based biosensor to analyze exomiRs in clinical serum samples for accurate AD diagnosis. To allow the detection of exomiRs in serum at attomolar levels, nanospaces (e.g., nanocrevice and nanocavity) were introduced into the nanostructures to dramatically increase the spectral sensitivity by adjusting the bending angle of the plasmonic nanostructure through sodium chloride concentration control. The developed biosensor classifies individuals into AD, mild cognitive impairment (MCI) patients, and HCs through profiling and quantifying exomiRs. Furthermore, integrating analysis expression patterns of multiple exosomal biomarkers improved serum-based diagnostic performance (average accuracy of 98.22%). Therefore, precise, highly sensitive serum-derived exosomal biomarker detection-based plasmonic biosensor has a robust capacity to predict the molecular pathologic of neurodegenerative disease, progression of cognitive decline, MCI/AD conversion, as well as early diagnosis and treatment.

Magneto-assisted enzymatic DNA walkers for simultaneous electrochemical detection of amyloid-beta oligomers and Tau.

DNA walkers have been widely explored and applied as biosensor elements to detect disease-related biomarkers. Traditional interface-anchored DNA walkers typically have a fixed swing arm range and an orientation of the preset track, which might complicate the design of a sensor system and limit its application in more scenes. We propose a simple electrochemical aptasensor to accurately detect Alzheimer's disease (AD) based on a nicking enzyme-powered DNA walker. In this method, bifunctional magnetic nanoparticles are used to identify and capture Aβ oligomers (AβO) and Tau and release the DNA walker. As the DNA walker moves freely on the surface of the electrode, the nicking enzymes circularly cleave and release the two signal substrate chains, significantly amplifying the signal. It has been demonstrated that the constructed sensor can sensitively detect AβO and Tau, and the combined analysis of dual markers improves the accuracy of AD diagnosis. Furthermore, this method can distinguish normal individuals from AD patients in real cerebrospinal fluid samples. The excellent performance of this biosensor makes it promising for clinical applications in diagnosing AD patients and prognosis assessment.

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis.

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.

Diagnosis of Alzheimer's disease using structure highlighting key slice stacking and transfer learning.

In recent years, two-dimensional convolutional neural network (2D CNN) have been widely used in the diagnosis of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI). However, due to the lack of targeted processing of the key slices of sMRI images, the classification performance of the CNN model needs to beimproved. Therefore, in this paper, we propose a key slice processing technique called the structural highlighting key slice stacking (SHKSS) technique, and we apply it to a 2D transfer learning model for ADclassification. Specifically, first, 3D MR images were preprocessed. Second, the 2D axial middle-layer image was extracted from the MR image as a key slice. Then, the image was normalized by intensity and mapped to the red, green, and blue (RGB) space, and histogram specification was performed on the obtained RGB image to generate the final three-channel image. The final three-channel image was input into a pretrained CNN model for AD classification. Finally, classification and generalization experiments were conducted to verify the validity of the proposedmethod. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set show that our SHKSS method can effectively highlight the structural information in MRI slices. Compared with existing key slice processing techniques, our SHKSS method has an average accuracy improvement of at least 26% on the same test data set, and it has better performance and generalizationability. Our SHKSS method not only converts single-channel images into three-channel images to match the input requirements of the 2D transfer learning model but also highlights the structural information of MRI slices to improve the accuracy of AD diagnosis.

Diagnose Alzheimer's disease by combining 3D discrete wavelet transform and 3D moment invariants

The early stage of Alzheimer's disease (AD) is called mild cognitive impairment (MCI). According to the final progression of the disease, it can be divided into progressive MCI (pMCI) and stable MCI (sMCI). Clinical treatment shows that early treatment of MCI stage can effectively delay the progression of the disease and even complete recovery. Therefore, accurate diagnosis of AD and its early stage play an important guiding role in the treatment and delay of AD progression. Previous studies have shown that the data of different modalities of AD can reflect different pathological changes. Therefore, the accuracy of combining multi-modality data to classify AD is better than that of using single-modality to classify AD. Based on this, an AD diagnosis method is proposed based on 3D discrete wavelet transform (3D-DWT) and 3D moment invariants (3D-MIs) features from multi-modalities images. First, automatic anatomical landmark (AAL) atlas is used to identify the brain regions of interest (ROIs) from magnetic resonance image (MRI) and positron emission tomography (PET). Next, for each ROI, the 3D-DWT and 3D-MIs extract methods are used to extract features. Finally, a deep neural network with stacked autoencoders (SAE) is trained to detect AD. The experimental results show that, compared with the state-of-the-art AD/MCI classification algorithms based on multi-modality features, this method can significantly improve the classification performance of pMCI and sMCI classification tasks, which has an important guiding role in the early diagnosis of AD.