Early Detection Of Alzheimer's Disease Research Articles

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.

The Association Between Postural Sway and Preclinical Alzheimer Disease Among Community-Dwelling Older Adults.

It is unknown whether older adults with preclinical Alzheimer disease (AD) experience changes in postural sway compared with those without preclinical AD. The purpose of this study was to understand the effect of dual tasking on standing balance, or postural sway, for people with and without preclinical AD. A cross-sectional analysis of baseline data from a longitudinal cohort study. Participants were cognitively normal older adults with and without preclinical AD. Postural sway (path length) was tested using a force plate under standard and dual task balance conditions. Dual task cost (DTC) was calculated to examine performance change in balance conditions. Logistic regression models were used to predict preclinical AD status as a function of DTC. 203 participants (65 preclinical AD+) were included. DTC for path length was significantly greater for participants with preclinical AD (DTC path length mean difference 19.8, 95% CI 2.6-37.0, t(201) = 2.29, p = .024). Greater DTC was significantly associated with increased odds of having preclinical AD (adjusted odds ratio for a 20-unit increase in DTC 1.16, 95% CI 1.02-1.32). Older adults with preclinical AD are more likely to demonstrate significantly greater DTC in postural sway than those without preclinical AD. Dual tasking should be integrated into balance and fall risk assessments and may inform early detection of preclinical AD.

Detection of Early Stage Alzheimer's Disease in Gradient-Based MR Images Using Deep Learning Methods

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

Predicting early Alzheimer’s with blood biomarkers and clinical features

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Design, Synthesis, and Characterization of Novel Styryl Dyes as Fluorescent Probes for Tau Aggregate Detection in Vitro and in Cells.

A series of novel styryl dye derivatives incorporating indolium and quinolinium core structures were successfully synthesized to explore their interacting and binding capabilities with tau aggregates in vitro and in cells. The synthesized dyes exhibited enhanced fluorescence emission in viscous environments due to the rotatable bond confinement in the core structure. Dye 4, containing a quinolinium moeity and featuring two cationic sites, demonstrated a 28-fold increase in fluorescence emission upon binding to tau aggregates. This dye could also stain tau aggregates in living cells, confirmed by cell imaging using confocal fluorescence microscopy. A molecular docking study was conducted to provide additional visualization and support for binding interactions. This work offers novel and non-cytotoxic fluorescent probes with desirable photophysical properties, which could potentially be used for studying tau aggregates in living cells, prompting further development of new fluorescent probes for early Alzheimer's disease detection.

Extracellular vesicle response following focused ultrasound-mediated blood–brain barrier opening for the detection of Alzheimer’s pathology

Focused ultrasound (FUS), in conjunction with systemically administered microbubbles, has been shown to induce transient and non-invasive blood–brain barrier opening (BBBO). Originally developed as a method of improving targeted drug delivery, FUS-BBBO has recently been proposed as a means of amplifying detection of disease biomarkers in the bloodstream. We hereby describe a method that can detect extracellular vesicles (EVs) after BBB opening in Alzheimer’s disease (AD) mice and patients. BBBO is shown to enhance the release of EVs with disease-specific cargo to the bloodstream when targeting amyloid affected regions and shown to be more sensitive than cell free DNA. In this presentation, we will describe the overall methodology and quantify the concentration and content of EVs isolated from the serum of mice following drug-free FUS-BBBO in both mice and patients. In AD mice and patients, an average EV concentration increase of 164% and 100% 1 h after FUS-BBBO containing both amyloid and tau was, respectively, found compared to 0% under sham conditions and highly correlated with BBBO volume. Whole genome RNA sequencing and mass spectrometry protein identification demonstrated inflammatory and proliferation gene upregulation. These findings highlight the potential of FUS-BBBO to increase EV serum concentration towards early 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.

Sound insights: The potential of hearables for early detection and continuous monitoring of Alzheimer’s disease

Early identification using non-invasive biological markers is crucial for detecting and treating Alzheimer's disease (AD). Hearables are intra-aural wearable devices equipped with in-ear microphones capable of capturing inner body signals, including heartbeat, breathing, and blinking. These non-verbal audio events give valuable insight into the individual's physiological state, offering a unique method to detect and monitor subtle changes in the body, which holds the potential for early disease detection. Central auditory (CA) tests, such as understanding speech in background noise, have shown promise in tracking AD’s effects on the central nervous system. They also provide sensitive, quantitative, and repeatable assessments over time. Therefore, this study will assess physiological signals captured by a hearable during central auditory assessment. Thirty-five participants with AD and mild cognitive impairment will be recruited, in addition to age-matched control participants. Hearables will be used to capture physiological data and perform CA tests such as hearing in noise test, triple digits test, dichotic digits test, and dichotic sentence identification test. Moreover, a picture description task, where participants will have to produce coherent speech that holds potential for tracking the decline in cognitive abilities, will be administered. The data collected will be used to build an open-access database serving as the foundation of future research on early detection and monitoring of AD using hearables.

Beyond CSF and Neuroimaging Assessment: Evaluating Plasma miR-145-5p as a Potential Biomarker for Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cause of dementia. New strategies for the early detection of MCI and sporadic AD are crucial for developing effective treatment options. Current techniques used for diagnosis of AD are invasive and/or expensive, so they are not suitable for population screening. Cerebrospinal fluid (CSF) biomarkers such as amyloid β1-42 (Aβ1-42), total tau (T-tau), and phosphorylated tau181 (P-tau181) levels are core biomarkers for early diagnosis of AD. Several studies have proposed the use of blood-circulating microRNAs (miRNAs) as potential novel early biomarkers for AD. We therefore applied a novel approach to identify blood-circulating miRNAs associated with CSF biomarkers and explored the potential of these miRNAs as biomarkers of AD. In total, 112 subjects consisting of 28 dementia due to AD cases, 63 MCI due to AD cases, and 21 cognitively healthy controls were included. We identified seven Aβ1-42-associated plasma miRNAs, six P-tau181-associated plasma miRNAs, and nine Aβ1-42-associated serum miRNAs. These miRNAs were involved in AD-relevant biological processes, such as PI3K/AKT signaling. Based on this signaling pathway, we constructed an miRNA-gene target network, wherein miR-145-5p has been identified as a hub. Furthermore, we showed that miR-145-5p performs best in the prediction of both AD and MCI. Moreover, miR-145-5p also improved the prediction performance of the mini-mental state examination (MMSE) score. The performance of this miRNA was validated using different datasets including an RT-qPCR dataset from plasma samples of 23 MCI cases and 30 age-matched controls. These findings indicate that blood-circulating miRNAs that are associated with CSF biomarkers levels and specifically plasma miR-145-5p alone or combined with the MMSE score can potentially be used as noninvasive biomarkers for AD or MCI screening in the general population, although studies in other AD cohorts are necessary for further validation.

Gene expression patterns of CRYM and SIGLEC10 in Alzheimer's disease: potential early diagnostic indicators.

Alzheimer's disease (AD) is a neurological condition that may lead to dementia as well as a slow and steady decline in cognitive ability. Finding early signs that may be used in the diagnosis of ADis still a difficult aim to achieve in the field of medical practice. The purpose of this research was to investigate to determineany differences in the gene expression patterns of crystallin mu (CRYM) and sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10) in whole blood samples obtained from fifty individuals who were diagnosed with ADand fifty individuals as a control group. When compared with controls, it was discovered that the expression of the CRYM gene was substantially decreased in AD patients, but the expression of the SIGLEC10 gene was significantly higher. A positive correlation between CRYM and SIGLEC10 was noticed solely in patients with AD. Furthermore, assessing the diagnostic value of these genes, CRYM and SIGLEC10 transcript levels displayed an area under the curve (AUC) of 0.74 and 0.81, respectively. These results suggest that alterations in CRYM and SIGLEC10 expression may be implicated in AD pathology and that these genes expression levels can potentially serve as biomarkers for early detection and diagnosis of AD. Nevertheless, further validation of these findings requires the inclusion of more extensive and heterogeneous cohorts. The findings derived from this study possess the capability to offer a significant contribution towards the progression of innovative diagnostic and therapeutic strategies for AD.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

Adjunct Methods for Alzheimer's Disease Detection: A Review of Auditory Evoked Potentials.

The auditory afferent pathway as a clinical marker of Alzheimer's disease (AD) has sparked interest in investigating the relationship between age-related hearing loss (ARHL) and AD. Given the earlier onset of ARHL compared to cognitive impairment caused by AD, there is a growing emphasis on early diagnosis and intervention to postpone or prevent the progression from ARHL to AD. In this context, auditory evoked potentials (AEPs) have emerged as a widely used objective auditory electrophysiological technique for both the clinical diagnosis and animal experimentation in ARHL due to their non-invasive and repeatable nature. This review focuses on the application of AEPs in AD detection and the auditory nerve system corresponding to different latencies of AEPs. Our objective was to establish AEPs as a systematic and non-invasive adjunct method for enhancing the diagnostic accuracy of AD. The success of AEPs in the early detection and prediction of AD in research settings underscores the need for further clinical application and study.

Enhancing Early Alzheimer's Disease Detection: Leveraging Pre-trained Networks and Transfer Learning

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early and accurate AD detection is crucial for timely intervention and improving patient outcomes. Lately, there have been notable advancements in using deep learning approaches to classify neuroimaging data associated with Alzheimer's disease. These methods have shown substantial progress in achieving accurate classification results. Nevertheless, the concept of end-to-end learning, which has the potential to harness the benefits of deep learning fully, has yet to garner extensive focus in the realm of neuroimaging. This is attributed mainly to the persistent challenge in neuroimaging, namely the limited data availability. This study employs neuroimages and Transfer Learning (TL) to identify early signs of AD and different phases of cognitive impairment. By employing transfer learning, the study uses Magnetic Resonance Imaging (MRI) images from the Alzheimer's Disease Neuroimaging (ADNI) database to classify images into various categories, such as Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). The classification task involves training and testing three pre-trained networks: VGG-19, ResNet-50, and Inception V3. The study evaluates the performance of these networks using the confusion matrix and its associated metrics. Among the three models, ResNet-50 achieves the highest recall rate of 99.25%, making it more efficient in detecting the early stages of AD development. The study further examines the performance of the pre-trained networks on a class-by-class basis using the parameters derived from the confusion matrix. This comprehensive analysis provides insights into how each model performs for different classes within the AD classification framework. Overall, the research underscores the potential of deep learning and transfer learning in advancing early AD detection and emphasizes the significance of utilizing pre-trained models for this purpose.

Latest advances in mechanisms of epileptic activity in Alzheimer's disease and dementia with Lewy Bodies.

Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) stand as the prevailing sources of neurodegenerative dementia, impacting over 55 million individuals across the globe. Patients with AD and DLB exhibit a higher prevalence of epileptic activity compared to those with other forms of dementia. Seizures can accompany AD and DLB in early stages, and the associated epileptic activity can contribute to cognitive symptoms and exacerbate cognitive decline. Aberrant neuronal activity in AD and DLB may be caused by several mechanisms that are not yet understood. Hyperexcitability could be a biomarker for early detection of AD or DLB before the onset of dementia. In this review, we compare and contrast mechanisms of network hyperexcitability in AD and DLB. We examine the contributions of genetic risk factors, Ca2+ dysregulation, glutamate, AMPA and NMDA receptors, mTOR, pathological amyloid beta, tau and α-synuclein, altered microglial and astrocytic activity, and impaired inhibitory interneuron function. By gaining a deeper understanding of the molecular mechanisms that cause neuronal hyperexcitability, we might uncover therapeutic approaches to effectively ease symptoms and slow down the advancement of AD and DLB.

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. Our results show that atrophic features are more considerable than morphological features in the early detection of AD.

Enhancing Early Alzheimer's Disease Detection Through Big Data and Ensemble Few-Shot Learning.

Alzheimer's disease is a severe brain disorder that causes harm in various brain areas and leads to memory damage. The limited availability of labeled medical data poses a significant challenge for accurate Alzheimer's disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer's disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. To address this challenge, our study leverages the power of Big Data in the form of pre-trained Convolutional Neural Networks (CNNs) within the framework of Few-Shot Learning (FSL) and ensemble learning. We propose an ensemble approach based on a Prototypical Network (ProtoNet), a powerful method in FSL, integrating various pre-trained CNNs as encoders. This integration enhances the richness of features extracted from medical images. Our approach also includes a combination of class-aware loss and entropy loss to ensure a more precise classification of Alzheimer's disease progression levels. The effectiveness of our method was evaluated using two datasets, the Kaggle Alzheimer dataset, and the ADNI dataset, achieving an accuracy of 99.72% and 99.86%, respectively. The comparison of our results with relevant state-of-the-art studies demonstrated that our approach achieved superior accuracy and highlighted its validity and potential for real-world applications in early Alzheimer's disease detection.

Alzheimer's Disease Diagnosis in the Preclinical Stage: Normal Aging or Dementia.

Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD.