Early Detection Of Alzheimer's Disease Research Articles

Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor

Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor

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.

Alzheimer’s Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN

Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.

Predicting Alzheimer’s progression in MCI: a DTI-based white matter network model

ObjectiveThis study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer’s disease (AD) in MCI patients.MethodsThis study enrolled 121 MCI patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.ResultsAPOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.ConclusionsA comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid β-protein (Aβ) indicators. The findings of this research reveal that individuals displaying reduced Aβ1-42/Aβ-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aβ1-42/Aβ-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

Associations Between Neuroinflammation-Related Conditions and Alzheimer's Disease: A Study of US Insurance Claims Data.

Early detection of Alzheimer's disease (AD) is a key component for the success of the recently approved lecanemab and aducanumab. Patients with neuroinflammation-related conditions are associated with a higher risk for developing AD. Investigate the incidence of AD among patients with neuroinflammation-related conditions including epilepsy, hemorrhage stroke, multiple sclerosis (MS), and traumatic brain injury (TBI). We used Optum's de-identified Clinformatics Data Mart Database (CDM). We derived covariate-matched cohorts including patients with neuroinflammation-related conditions and controls without the corresponding condition. The matched cohorts were: 1) patients with epilepsy and controls (N = 67,825 matched pairs); 2) patients with hemorrhage stroke and controls (N = 81,510 matched pairs); 3) patients with MS and controls (N = 9,853 matched pairs); and 4) patients TBI and controls (N = 104,637 matched pairs). We used the Cox model to investigate the associations between neuroinflammation-related conditions and AD. We identified that epilepsy, hemorrhage stroke, and TBI were associated with increased risks of AD in both males and females (hazard ratios [HRs]≥1.74, p <  0.001), as well as in gender- and race-conscious subpopulations (HRs≥1.64, p <  0.001). We identified that MS was associated with increased risks of AD in both males and females (HRs≥1.47, p≤0.004), while gender- and race-conscious subgroup analysis shown mixed associations. Patients with epilepsy, hemorrhage stroke, MS, and/or TBI are associated with a higher risk of developing AD. More attention on cognitive status should be given to older patients with these conditions.

New plasma LC-MS/MS assays for the quantitation of beta-amyloid peptides and identification of apolipoprotein E proteoforms for Alzheimer's disease risk assessment.

Early detection of Alzheimer's disease (AD) represents an unmet clinical need. Beta-amyloid (Aβ) plays an important role in AD pathology, and the Aβ42/40 peptide ratio is a good indicator for amyloid deposition. In addition, variants of the apolipoprotein E (APOE) gene are associated with variable AD risk. Here, we describe the development and validation of high-throughput liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays for plasma Aβ40 and Aβ42 quantitation, as well as apolipoprotein E (ApoE) proteotype determination as a surrogate for APOE genotype. Aβ40 and Aβ42 were simultaneously immunoprecipitated from plasma, proteolytically digested, and quantitated by LC-MS/MS. ApoE proteotype status was qualitatively assessed by targeting tryptic peptides from the ApoE2, ApoE3, and ApoE4 proteoforms. Both assays were validated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Within-run precision was 1.8%-4.2% (Aβ40), 1.9%-7.2% (Aβ42), and 2.6%-8.3% (Aβ42/40 ratio). Between-run precision was 3.5%-5.9% (Aβ40), 3.8%-8.0% (Aβ42), and 3.3%-8.7% (Aβ42/40 ratio). Both Aβ40 and Aβ42 were linear from 10 to 2500 pg/mL. Identified ApoE proteotypes had 100% concordance with APOE genotypes. We have developed a precise, accurate, and sensitive high-throughput LC-MS/MS assay for plasma Aβ40, Aβ42, and proteoforms of ApoE.

Early Detection of Alzheimers Disease using Deep Learning

Alzheimer’s disease (AD) poses a significant challenge to global healthcare systems due to its progressive nature and impact on patient’s lives. Accurate and early detection of AD is crucial for timely intervention and management. In this paper, we propose the use of deep learning models, including Convolutional Neural Networks (CNNs), MobileNet, and VGG16 for the classification of Magnetic Resonance Imaging (MRI) scans into different AD stages. Index Terms—Alzheimer’s disease , Deep Learning , Convolu- tional Neural Networks(CNN) , MobileNet , VGG16 , MRI scans

Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio.

Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15-20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs' superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.

Analysis of EEG characteristics in Alzheimer’s disease

Alzheimer’s disease (AD) brings a heavy burden to society and families because of the high misdiagnosis rate, prevalence rate, and disability rate. At present, the treatment of AD still faces great challenges. Early intervention can effectively delay the disease progression and improve the burden of symptom care. However, the key to early intervention is accurately identifying and assessing the disease. The examination is difficult to be widely used because of its high invasive cost. There is an urgent need for a simple, convenient, and non-invasive diagnosis of AD. Studies have shown that the brain number of AD patients is slower than that of healthy people, and the complexity of EEG is disturbed. Clinical practice has proved that the specificity and sensitivity of α, β, δ, θ absolute power and α/θ absolute power ratio, especially θ absolute power and α/θ absolute power ratio in the model, are important electrophysiological indexes for AD diagnosis EEG. Non-invasive and economical electrophysiological techniques can characterize and identify the EEG characteristics of AD patients from different angles. They are relatively simple to implement, and patients have a high degree of cooperation. Therefore, EEG, as a diagnostic method of AD, can improve the accuracy of differential diagnosis and early detection of AD and quantify the severity of the disease. It has the advantage of non-invasive convenience and price, which is of great significance for early diagnosis of AD.

Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS).

The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups. The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.

A Comprehensive Review of Advancement in Speech-Based Approach for Alzheimer Disease Detection in India

Alzheimer’s Disease (AD), accounting for 60%-70% of dementia cases, is poised to afflict India as the second-largest country by mid-century, as indicated by a recent World Health Organization (WHO) study. The socio-economic landscape in India presents unique challenges, with a population that may not be adequately educated on allocating resources for healthcare beyond the age of 60, especially for conditions like Alzheimer's, which currently lacks a definitive cure. Addressing this impending health crisis necessitates innovative, early diagnosis and cost-effective solutions. This comprehensive review paper explores the potential of utilizing speech data analysis for the early detection of Alzheimer’s Disease (AD), offering a pragmatic approach to mitigate the severity of cases. By harnessing speech data, the aim is to diagnose the condition in its early stages, minimizing costs and enabling timely interventions to impede its progression. Early diagnosis is paramount, as it opens doors to effective treatments and preventative measures. The paper systematically reviews various processes involved in developing a mathematical model for the early detection of Alzheimer’s Disease (AD). It delves into key aspects such as data collection methodologies, pre-processing techniques, feature extraction methods, and diverse classifiers. The intention is to provide a thorough understanding of the intricacies involved in creating an accurate and reliable model for early Alzheimer's detection. In light of Alzheimer’s Disease (AD) representing a substantial majority of dementia cases, this review paper serves as a valuable resource for researchers, healthcare professionals, and policymakers. It fosters a deeper comprehension of potential avenues for leveraging speech data specifically in the context of Alzheimer's. As we confront the impending surge in Alzheimer's cases in India, this comprehensive review work contributes to the foundation of knowledge needed to develop scalable and accessible solutions, making a substantial impact on public health.

Cognitive impairment, neurodegenerative disorders, and olfactory impairment: A literature review.

&lt;br&gt;&lt;b&gt;Introduction:&lt;/b&gt; The early detection and diagnosis of dementia are of key importance in treatment, slowing disease progression, or suppressing symptoms. The possible role of changes in the sense of smell is considered with regard to potential markers for early detection of Alzheimer's disease (AD).&lt;/br&gt; &lt;br&gt;&lt;b&gt;Materials and methods:&lt;/b&gt; A literature search was conducted using the electronic databases PubMed, Scopus, and Web of Science between May 30, 2022 and August 2, 2022. The term "dementia" was searched with keyword combinations related to olfaction.&lt;/br&gt; &lt;br&gt;&lt;b&gt;Results:&lt;/b&gt; A total of 1,288 records were identified through the database search. Of these articles, 49 were ultimately included in the analysis. The results showed the potential role of changes in the sense of smell as potential biomarkers for early detection of AD. Multiple studies have shown that olfactory impairment may be observed in patients with AD, PD, MCI, or other types of dementia. Even though smell tests are able to detect olfactory loss caused by neurodegenerative diseases, they cannot reliably distinguish between certain diseases.&lt;/br&gt; &lt;br&gt;&lt;b&gt;Conclusions:&lt;/b&gt; In individuals with cognitive impairment or neurodegenerative diseases, olfactory assessment has repeatedly been reported to be used for early diagnosis, but not for differential diagnosis.&lt;/br&gt.

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN-based approaches in leveraging raw MRI images for accurate and early detection of AD.

Neuro-cogniguard: a Deep Learning Approach for Early Alzheimer's Detection

Neuro-cogniguard: a Deep Learning Approach for Early Alzheimer's Detection

Alzheimer Disease Using Machine Learning

The successful development of amyloid-based biomarkers and tests for Alzheimer’s disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) &gt; 80%, specificity (SP) &gt; 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.              

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

AI-based tool for early detection of Alzheimer's disease

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

EMPOWER! (Brain Health Education in Minority Communities to Promote Knowledge about Early Detection of Alzheimer's Disease and Research Participation) Curriculum

EMPOWER! (Brain Health Education in Minority Communities to Promote Knowledge about Early Detection of Alzheimer's Disease and Research Participation) Curriculum

Early Detection of Alzheimers Disease through Deep Learning in MRI Scans

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory impairment, leading to dementia and irreversible decline in cognitive abilities. This disease causes the person to suffer from memory loss, unusual behaviour, and language problems. The initial symptoms, such as episodic memory impairment and the navigational problem of the patient, are typical variants. In the proposed system we are using The Deep Learning Models as Modified CNN along with MobileNet, and VGG16 algorithms and classify the images as either Mild Demented or Moderate Demented or Non-Demented or Very Mild Demented. In this study we are using Augmented ADNI dataset which consists of 40000 images. This can be utilized in the future to classify the types of different classifications easily which is easy to find out the infections in initial stages and can be cured in the initial stages only.