Detection Of Alzheimer's Disease Research Articles

Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.

Iterative Design of Near-Infrared Fluorescent Probes for Early Diagnosis of Alzheimer's Disease by Targeting Aβ Oligomers.

Amyloid-β oligomers (AβOs), crucial toxic proteins in early Alzheimer's disease (AD), precede the formation of Aβ plaques and cognitive impairment. In this context, we present our iterative process for developing novel near-infrared fluorescent (NIRF) probes specifically targeting AβOs, aimed at early AD diagnosis. An initial screening identified compound 18 as being highly selective for AβOs. Subsequent analysis revealed that compound 20 improved serum stability while retaining affinity for AβOs. The most promising iteration, compound 37, demonstrated exceptional qualities: a high affinity for AβOs, emission in the near-infrared region, and good biocompatibility. Significantly, ex vivo double staining indicated that compound 37 detected AβOs in AD mouse brain and in vivo imaging experiments showed that compound 37 could differentiate between 4-month-old AD mice and age-matched wild-type mice. Therefore, compound 37 has emerged as a valuable NIRF probe for early detection of AD and a useful tool in exploring AD's pathological mechanisms.

The Role of Non-coding RNAs in Alzheimer's Disease: Pathogenesis, Novel Biomarkers, and Potential Therapeutic Targets.

Long non-coding RNAs (IncRNAs) are regulatory RNA transcripts that have recently been associated with the onset of many neurodegenerative illnesses, including Alzheimer's disease (AD). Several IncRNAs have been found to be associated with AD pathophysiology, each with a distinct mechanism. In this review, we focused on the role of IncRNAs in the pathogenesis of AD and their potential as novel biomarkers and therapeutic targets. Searching for relevant articles was done using the PubMed and Cochrane library databases. Studies had to be published in full text in English in order to be considered. Some IncRNAs were found to be upregulated, while others were downregulated. Dysregulation of IncRNAs expression may contribute to AD pathogenesis. Their effects manifest as the synthesis of beta-amyloid (Aβ) plaques increases, thereby altering neuronal plasticity, inducing inflammation, and promoting apoptosis. Despite the need for more investigations, IncRNAs could potentially increase the sensitivity of early detection of AD. Until now, there has been no effective treatment for AD. Hence, InRNAs are promising molecules and may serve as potential therapeutic targets. Although several dysregulated AD-associated lncRNAs have been discovered, the functional characterization of most lncRNAs is still lacking.

Deep Learning Approach for Early Detection of Alzheimers Disease

Abstract: Alzheimer’s disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages.

Design and synthesis of hemicyanine-based NIRF probe for detecting Aβ aggregates in Alzheimer’s disease

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, has garnered increased attention due to its substantial economic burden and the escalating global aging phenomenon. Amyloid-β deposition is a key pathogenic marker observed in the brains of Alzheimer’s sufferers. Based on real-time, safe, low-cost, and commonly used, near-infrared fluorescence (NIRF) imaging technology have become an essential technique for the detection of AD in recent years. In this work, NIRF probes with hemicyanine structure were designed, synthesized and evaluated for imaging Aβ aggregates in the brain. We use the hemicyanine structure as the parent nucleus to enhance the probe’s optical properties. The introduction of PEG chain is to improve the probe’s brain dynamice properties, and the alkyl chain on the N atom is to enhance the fluorescence intensity of the probe after binding to the Aβ aggregates as much as possible. Among these probes, Z2, Z3, Z6, X3, X6 and T1 showed excellent optical properties and high affinity to Aβ aggregates (Kd = 24.31 ∼ 59.60 nM). In vitro brain section staining and in vivo NIRF imaging demonstrated that X6 exhibited superior discrimination between Tg mice and WT mice, and X6 has the best brain clearance rate. As a result, X6 was identified as the optimal probe. Furthermore, the docking theory calculation results aided in describing X6′s binding behavior with Aβ aggregates. As a high-affinity, high-selectivity, safe and effective probe of targeting Aβ aggregates, X6 is a promising NIRF probe for in vivo detection of Aβ aggregates in the AD brain.

Alzhimer Detection Using CNN

Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disease. It is most popular Alzheimer disease symptoms include trouble thinking, making judgment and decision, not able to do familiar task it’s going to also cause alteration in personality and behavior .The factor of AD development is poorly known. As the sickness advances, an individual with Alzheimer disease will develop severe amnesia and lose the capacity to perform everyday activation. This incurable disease is mainly found within elderly people. Neuroimaging technique like MRI and PET scan are used for AD detection. For better result multi model neuroimaging technique are used with DL algorithm for Alzheimer classification process. In this project we are going to use MRI along with PET scan and CNN (convolution neural network) for image classification into normal cognitive (NC).

Potential Application of MicroRNAs and Some Other Molecular Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) is the world's most common neurodegenerative disease, expected to affect up to one-third of the elderly population in the near future. Among the major challenges in combating AD are the inability to reverse the damage caused by the disease, expensive diagnostic tools, and the lack of specific markers for the early detection of AD. This paper highlights promising research directions for molecular markers in AD diagnosis, including the diagnostic potential of microRNAs. The latest molecular methods for diagnosing AD are discussed, with particular emphasis on diagnostic techniques prior to the appearance of full AD symptoms and markers detectable in human body fluids. A collection of recent studies demonstrates the promising potential of molecular methods in AD diagnosis, using miRNAs as biomarkers. Up- or downregulation in neurodegenerative diseases may not only provide a new diagnostic tool but also serve as a marker for differentiating neurodegenerative diseases. However, further research in this direction is needed.

Progress on early diagnosing Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.

CCADD: An online webserver for Alzheimer's disease detection from brain MRI

Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.

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.

Utilizing Profound Learning Methods for Alzheimer's Infection Discovery: An Exhaustive Survey

Alzheimer’s disease (AD) is a major public health issue around the world. It is a progressive disease that causes memory loss and cognitive decline. Early detection is essential for early intervention and treatment of AD. In recent years, there has been a surge in the use of deep learning techniques to analyze complex medical data. This includes neuroimaging data as well as genetic data. This has led to promising advances in the detection of AD. In this paper, we will review the application of various deep learning methods in the field of Alzheimer’s Disease. We will look at the challenges that traditional diagnostic methods face and how these challenges can be overcome by using deep learning approaches. We will also look at different deep learning architectures and data modalities and techniques used in the field of AD detection. Finally, we will look at limitations and future direction of the field to help researchers and practitioners to develop more accurate and effective AD detection methods. Keywords: Alzheimer's Disease, deep learning, neuroimaging, biomarkers, early detection, diagnosis.

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.

A LeViT–EfficientNet-Based Feature Fusion Technique for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is a progressive neurodegenerative condition. It causes cognitive impairment and memory loss in individuals. Healthcare professionals face challenges in detecting AD in its initial stages. In this study, the author proposed a novel integrated approach, combining LeViT, EfficientNet B7, and Dartbooster XGBoost (DXB) models to detect AD using magnetic resonance imaging (MRI). The proposed model leverages the strength of improved LeViT and EfficientNet B7 models in extracting high-level features capturing complex patterns associated with AD. A feature fusion technique was employed to select crucial features. The author fine-tuned the DXB using the Bayesian optimization hyperband (BOHB) algorithm to predict AD using the extracted features. Two public datasets were used in this study. The proposed model was trained using the Open Access Series of Imaging Studies (OASIS) Alzheimer’s dataset containing 86,390 MRI images. The Alzheimer’s dataset was used to evaluate the generalization capability of the proposed model. The proposed model obtained an average generalization accuracy of 99.8% with limited computational power. The findings highlighted the exceptional performance of the proposed model in predicting the multiple types of AD. The recommended integrated feature extraction approach has supported the proposed model to outperform the state-of-the-art AD detection models. The proposed model can assist healthcare professionals in offering customized treatment for individuals with AD. The effectiveness of the proposed model can be improved by generalizing it to diverse datasets.

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

Introduction: 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. Methods: 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. Results: 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. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

Intelligence model for Alzheimer’s disease detection with optimal trained deep hybrid model

Alzheimer’s disease (AD), a neurodegenerative disorder, is the most common cause of dementia and continuing cognitive deficits. Since there are more cases each year, AD has grown to be a serious social and public health issue. Early detection of the diagnosis of Alzheimer’s and dementia disease is crucial, as is giving them the right care. The importance of early AD diagnosis has recently received a lot of attention. The patient cannot receive a timely diagnosis since the present methods of diagnosing AD take so long and are so expensive. That’s why we created a brand-new AD detection method that has four steps of operation: pre-processing, feature extraction, feature selection, and AD detection. During the pre-processing stage, the input data is pre-processed using an improved data normalization method. Following the pre-processing, these pre-processed data will go through a feature extraction procedure where features including statistical, enhanced entropy-based and mutual information-based features will be extracted. The appropriate features will be chosen from these extracted characteristics using the enhanced Chi-square technique. Based on the selected features, a hybrid model will be used in this study to detect AD. This hybrid model combines classifiers like Long Short Term Memory (LSTM) and Deep Maxout neural networks, and the weight parameters of LSTM and Deep Maxout will be optimized by the Self Updated Shuffled Shepherd Optimization Algorithm (SUSSOA). Our Proposed SUSSOA-based method’s statistical analysis of best values such as 57%, 53%, 28%, 25%, and 21% is higher than the other models like SSO, BMO, HGS, BRO, BES, and ISSO respectively.

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.

Alzheimer’s disease classification algorithm based on fusion of channel attention and densely connected networks

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD.

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.

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.