Early Detection Of Alzheimer's Disease Research Articles

Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.

VoiceAD: A multilingual speech based classifier for early detection of Alzheimers disease

Alzheimer's Disease (AD) causes memory decline beyond normal aging and is the most common cause of dementia. Early detection and intervention can delay the progression of AD and alleviate symptoms. However, high expense, insensitivity, and inconvenience are among various deterrents to traditional diagnosis methods. Dementia impacts more than 55 million people globally, with this number set to double every 20 years. Much of this increase will be in low- and middle-income countries which often lack the resources for early diagnosis. Recently speech has emerged as an effective digital biomarker for AD detection. However, most studies in this area are limited to English speech due to lack of suitable data in other languages. This study aims to construct a speech-based classifier (VoiceAD) that can be trained on English data and still maintain its performance on other languages. Acoustic features were extracted from English, Spanish, and Greek data from DementiaBank with the eGeMAPS v2.0 feature set. Subsequently, an Extra Trees model was employed for classification. VoiceADs performance for English and Greek surpassed that of the baseline model and model incorporating only demographic data while the Spanish performance was lower due to task effect. In addition, VoiceADs satisfactory generalizability and interpretability positions it as a promising candidate for clinical applications. It can be implemented as a free screening tool such as a mobile application to monitor AD risk. VoiceADs crosslingual applicability can its enable worldwide deployment, especially in settings where traditional methods of diagnosis are less attainable.

Alzheimer's disease: from early pathogenesis to novel therapeutic approaches.

The mainstay behind Alzheimer's disease (AD) remains unknown due to the elusive pathophysiology of the disease. Beta-amyloid and phosphorylated Tau is still widely incorporated in various research studies while studying AD. However, they are not sufficient. Therefore, many scientists and researchers have dug into AD studies to deliver many innovations in this field. Many novel biomarkers, such as phosphoglycerate-dehydrogenase, clusterin, microRNA, and a new peptide ratio (Aβ37/Aβ42) in cerebral-spinal fluid, plasma glial-fibrillary-acidic-protein, and lipid peroxidation biomarkers, are mushrooming. They are helping scientists find breakthroughs and substantiating their research on the early detection of AD. Neurovascular unit dysfunction in AD is a significant discovery that can help us understand the relationship between neuronal activity and cerebral blood flow. These new biomarkers are promising and can take these AD studies to another level. There have also been big steps forward in diagnosing and finding AD. One example is self-administered-gerocognitive-examination, which is less expensive and better at finding AD early on than mini-mental-state-examination. Quantum brain sensors and electrochemical biosensors are innovations in the detection field that must be explored and incorporated into the studies. Finally, novel innovations in AD studies like nanotheranostics are the future of AD treatment, which can not only diagnose and detect AD but also offer treatment. Non-pharmacological strategies to treat AD have also yielded interesting results. Our literature review spans from 1957 to 2022, capturing research and trends in the field over six decades. This review article is an update not only on the recent advances in the search for credible biomarkers but also on the newer detection techniques and therapeutic approaches targeting AD.

Attitudes and Preferences Towards Screening for Dementia with a Focus on Ethnic Minority and Low Socio-Economic Groups: A Systematic Review of Research Studies Written in the English Language.

Increased understanding of dementia risk-reduction and early detection of Alzheimer's disease and related disorders has spurred interest in the identification of risks for dementia, underlying putative biologies, or dementia itself. Implementation of such approaches require acceptability to the public. Research prior to 2012 indicated limited acceptability for population dementia screening. The changing landscape of dementia prevention research may influence recent perceptions. Additionally, perspectives from underserved populations, such as ethnic minorities and low socio-economic groups, are lacking. In this systematic review, we sought published studies since 2012 on attitudes and preferences of people with dementia, carers and the general public from ethnic minorities and low socio-economic groups regarding dementia screening. This review was preregistered on PROSPERO (CRD42023384115) and followed PRISMA guidelines. Key search terms were entered into five databases. Articles were included if they focused on population or risk screening for dementia via primary/community care-based assessments, and which included majority ethnic minority or low socio-economic groups or discretely considered these groups in data analysis. Data were synthesized narratively. Seven studies reported perspectives of ethnic minorities regarding dementia screening; one study included people from low socio-economic groups. Results indicated that participants from ethnic minorities were willing to undergo dementia screening. Predictors of willingness included belief in benefits, desire to boost diversity, and to implement lifestyle changes. Unwillingness was associated with anxiety regarding results. Although there seems to be high acceptability for screening in the studied groups, more research is necessary to explore the practical considerations for screening such as cultural and economic barriers, trust, and post-screening actions.

The Brain-Gut Axis, an Important Player in Alzheimer and Parkinson Disease: A Narrative Review.

Neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are severe age-related disorders with complex and multifactorial causes. Recent research suggests a critical link between neurodegeneration and the gut microbiome, via the gut-brain communication pathway. This review examines the role of trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, in the development of AD and PD, and investigates its interaction with microRNAs (miRNAs) along this bidirectional pathway. TMAO, which is produced from dietary metabolites like choline and carnitine, has been linked to increased neuroinflammation, protein misfolding, and cognitive decline. In AD, elevated TMAO levels are associated with amyloid-beta and tau pathologies, blood-brain barrier disruption, and neuronal death. TMAO can cross the blood-brain barrier and promote the aggregation of amyloid and tau proteins. Similarly, TMAO affects alpha-synuclein conformation and aggregation, a hallmark of PD. TMAO also activates pro-inflammatory pathways such as NF-kB signaling, exacerbating neuroinflammation further. Moreover, TMAO modulates the expression of various miRNAs that are involved in neurodegenerative processes. Thus, the gut microbiome-miRNA-brain axis represents a newly discovered mechanistic link between gut dysbiosis and neurodegeneration. MiRNAs regulate the key pathways involved in neuroinflammation, oxidative stress, and neuronal death, contributing to disease progression. As a direct consequence, specific miRNA signatures may serve as potential biomarkers for the early detection and monitoring of AD and PD progression. This review aims to elucidate the complex interrelationships between the gut microbiota, trimethylamine-N-oxide (TMAO), microRNAs (miRNAs), and the central nervous system, and the implications of these connections in neurodegenerative diseases. In this context, an overview of the current neuroradiology techniques available for studying neuroinflammation and of the animal models used to investigate these intricate pathologies will also be provided. In summary, a bulk of evidence supports the concept that modulating the gut-brain communication pathway through dietary changes, the manipulation of the microbiome, and/or miRNA-based therapies may offer novel approaches for implementing the treatment of debilitating neurological disorders.

Could Accelerated Long-Term Forgetting Be a Feature of the Higher Rate of Memory Complaints Associated with Subjective Cognitive Decline? An Exploratory Study.

Recently, subjective cognitive decline (SCD) was proposed as an early risk factor for future Alzheimer's disease (AD). In this study, we investigated whether accelerated long-term forgetting (ALF), assessed with extended testing intervals than those adopted in clinical practice, might be a cognitive feature of SCD. Using an explorative MRI analysis of the SCD sample, we attempted to investigate the areas most likely involved in the ALF pattern. We recruited 31 individuals with SCD from our memory clinic and subdivided them based on their rate of memory complaints into mild SCDs (n = 18) and severe SCDs (n = 13). A long-term forgetting procedure, involving the recall of verbal and visuo-spatial material at four testing delays (i.e., immediate, 30 min, 24 h, and 7 days post-encoding) was used to compare the two sub-groups of SCDs with a healthy control group (HC; n = 16). No significant between-group difference was found on the standard neuropsychological tests, nor in the immediate and 30 min recall of the experimental procedure. By contrast, on the verbal test severe SCDs forgot significantly more than HCs in the prolonged intervals (i.e., 24 h and 7 days), with the greatest decline between 30 min and 24 h. Finally, in the whole SCD sample, we found significant associations between functional connectivity values within some cortical networks involved in memory (default mode network, salience network, and fronto-parietal network) and verbal long-term measures. Our preliminary findings suggest that long-term forgetting procedures could be a sensitive neuropsychological tool for detecting memory concerns in SCDs, contributing to early AD detection.

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations.Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification.Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network.

Introduction: Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations. Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification. Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer's risk-enhanced sample.

Understanding longitudinal change in key plasma biomarkers will aid in detecting presymptomatic Alzheimer's disease (AD). Serial plasma samples from 424 Wisconsin Registry for Alzheimer's Prevention participants were analyzed for phosphorylated-tau217 (p-tau217; ALZpath) and other AD biomarkers, to study longitudinal trajectories in relation to disease, health factors, and cognitive decline. Of the participants, 18.6% with known amyloid status were amyloid positive (A+); 97.2% were cognitively unimpaired (CU). In the CU, amyloid-negative (A-) subset, plasma p-tau217 levels increased modestly with age but were unaffected by body mass index and kidney function. In the whole sample, average p-tau217 change rates were higher in those who were A+ (e.g., simple slopes(se) for A+ and A- at age 60 were 0.232(0.028) and 0.038(0.013))). High baseline p-tau217 levels predicted faster preclinical cognitive decline. p-tau217 stands out among markers for its strong association with disease and cognitive decline, indicating its potential for early AD detection and monitoring progression. Phosphorylated-tau217 (p-tau217) trajectories were significantly different in people who were known to be amyloid positive. Subtle age-related trajectories were seen for all the plasma markers in amyloid-negative cognitively unimpaired. Kidney function and body mass index were not associated with plasma p-tau217 trajectories. Higher plasma p-tau217 was associated with faster preclinical cognitive decline.

Effect of Health Educational Program on Early Detection of Alzheimer Disease among Elderly patients with Diabetes type I

Effect of Health Educational Program on Early Detection of Alzheimer Disease among Elderly patients with Diabetes type I

An Implementation of Effective CNN Model for AD Detection

This paper focuses on detecting Alzheimer’s Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

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.

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

ANALYZE-AD: A comparative analysis of novel AI approaches for early Alzheimer’s detection

Alzheimer’s disease, characterized by progressive and irreversible deterioration of cognitive functions, represents a significant health concern, particularly among older adults, as it stands as the foremost cause of dementia. Despite its debilitating nature, early detection of Alzheimer’s disease holds considerable advantages for affected individuals. This study investigates machine-learning methodologies for the early diagnosis of Alzheimer’s disease, utilizing datasets sourced from OASIS and ADNI. The initial classification methods consist of a 5-class ADNI classification and a 3-class OASIS classification. Three unique methodologies encompass binary-class inter-dataset models, which involve training on a single dataset and subsequently testing on another dataset for both ADNI and OASIS datasets. Additionally, a hybrid dataset model is also considered. The proposed methodology entails the concatenation of both datasets, followed by shuffling and subsequently conducting training and testing on the amalgamated dataset. The findings demonstrate impressive levels of accuracy, as Light Gradient Boosting Machine (LGBM) achieved a 99.63% accuracy rate for 5-class ADNI classification and a 95.75% accuracy rate by Multilayer Perceptron (MLP) for 3-class OASIS classification, both when hyperparameter tweaking was implemented. The K-nearest neighbor algorithm demonstrated exceptional performance, achieving an accuracy of 87.50% in ADNI-OASIS (2 Class) when utilizing the Select K Best method. The Gaussian Naive Bayes algorithm demonstrated exceptional performance in the OASIS-ADNI approach, attaining an accuracy of 77.97% using Chi-squared feature selection. The accuracy achieved by the Hybrid method, which utilized LGBM with hyperparameter optimization, was 99.21%. Furthermore, the utilization of Explainable AI approaches, particularly Lime, was implemented in order to augment the interpretability of the model.

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.

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.

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