Detection Of Alzheimer's Disease Research Articles

Human Leukocyte Antigen and microRNAs as Key Orchestrators of Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review

The expression of inflamma-miRs and human leukocyte antigen (HLA) haplotypes could indicate mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We used international databases to conduct a systematic review of studies on HLA variants and a meta-analysis of research on microRNAs (miRNAs). We aimed to analyze the discriminative value of HLA variants and miRNAs in MCI, AD and controls to evaluate the protective or causative effect of HLA in cognitive decline, establish the role of miRNAs as biomarkers for the early detection of AD, and find a possible link between miRNAs and HLA. Statistical analysis was conducted using Comprehensive Meta-analysis software, version 2.2.050 (Biostat Inc., Englewood, NJ, USA). The effect sizes were estimated by the logarithm base 2 of the fold change. The systematic review revealed that some HLA variants, such as HLA-B*4402, HLA-A*33:01, HLA-A*33:01, HLA-DPB1, HLA-DR15, HLA-DQB1*03:03, HLA-DQB1*06:01, HLA-DQB1*03:01, SNPs on HLA-DRB1/DQB1, and HLA-DQA1, predisposed to cognitive decline before the occurrence of AD, while HLA-A1*01, HLA-DRB1∗13:02, HLA-DRB1*04:04, and HLA-DRB1*04:01 demonstrated a protective role. The meta-analysis identified let-7 and miR-15/16 as biomarkers for the early detection of AD. The association between these two miRNA families and the HLA variants that predispose to AD could be used for the early screening and prevention of MCI.

A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.

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.

Diagnostics of Alzheimer’s disease using fluorescent nanodiamond-based spin-enhanced lateral flow immunoassay

Fluorescent nanodiamond (FND) has been regarded as a superior alternative reporter for lateral flow immunoassays. The negatively charged nitrogen-vacancy (NV−) center in FND is a point defect with unique magneto-optical properties, giving outstanding characteristics to detect biomarkers of diseases, including Alzheimer’s disease (AD). AD is the most common form of dementia, which might ultimately result in complete brain failure and, eventually, death. The two most commonly used techniques to detect brain changes caused by AD are brain imaging and lumbar puncture. However, brain imaging and lumbar puncture are costly and time-consuming. Thus, we have developed Spin-Enhanced Lateral Flow Immunoassay (SELFIA) for in vitro AD diagnostics utilizing the electron spin properties of the NV−-centers in FND to achieve background-free ultrasensitive detection. To detect the pTau protein (a critical AD biomarker), we first conjugated FND to anti-pTau antibodies through non-covalent conjugation. We employed a sandwich assay to enhance sensitivity and specificity by immobilizing capture antibodies on the membrane so that antibody-antigen–antibody immunocomplexes would be formed. Our FND-based SELFIA only requires approximately 30 min to reach a detection limit of 7 pg/mL, where the threshold for positivity was 101.95 pg/mL for cerebrospinal fluid pTau217. This is the first study that utilized FND-based SELFIA for AD detection. The current SELFIA platform is rapid, easy to use, and affordable. This study not only represents a significant innovation but also opens new avenues for early 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.

Intelligent Data Processing for Alzheimer's Disease Using Deep Learning

Background: With the advancement in Alzheimer's disease (AD) brain, cells start to deteriorate, which eventually creates physical dependency and mental instability that interferes with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is early detection and prevention. Although many studies have investigated the usefulness of deep learning in AD detection, relatively few have focused on the necessary image preprocessing processes, which are essential to any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes into account a diverse handful of prominent features is required. Method: This paper focuses on improving MRI-based AD detection by incorporating image enhancement approaches and deep hybrid learning into a fused framework to harness the power of multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore, the mRMR approach was used to optimise the acquired features, followed by classification via a stack of multiple ML classifiers to predict the target class. Results: The proposed architecture based on feature fusion strategy and ensemble learning resulted in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented model outperformed individual deep CNN architectures. Conclusion: Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration of the presented technique upon significant regions of the image and can assist medical professionals in decoding the decisions. The presented technique exeplifies the potency and constancy of categorizing Alzheimer's disease.

Deep CNN-based Classification of Brain MRI Images for Alzheimer’s Disease Diagnosis

As the leading cause of dementia worldwide, Alzheimer's disease afflicts millions, with progressively impaired abilities to carry out daily activities or communicate and even recognize faces. Although the cause behind lupus is not fully understood, it probably reflects lifestyle choices and environmental factors as well as genetic propensity. The largest obstacles in the diagnosis of these diseases are their often subtle early manifestations and absence of sensitive detection paradigms. Deep-learning algorithms first came to the forefront of medical imaging just a few years ago and were celebrated as sophisticated diagnostic aids, able to spot subtle signs in scans usually hidden from human eyes. We are benefitting from the use of these state-of-the-art algorithms to improve Alzheimer's detection, with one of the largest MRI datasets available today (more than 86,000 images) being used to train our model. In view of this vast data set, it was appreciably combined one to be accurate-centric diagnostic tool. The performance of our novel deep learning model is strong and provided state-of-the-art validation accuracy (99.63%), surpassing existing models These figures highlight the great promise of our model as a verifiable method for detecting early-stage Alzheimer's disease - a significant concern in controlling and managing disease progression. Our research truly is a major step forward in the field of Alzheimer's disease diagnosis by employing cutting-edge deep learning techniques. Early diagnosis allows for better treatment and lower disease burden that can prevent morbidity, mortality and even change many patient outcomes. This is a considerable improvement toward diagnosing Alzheimer's disease with the help of artificial intelligence and presents an expectation for more exact and timely finding.

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.

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.

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.

Ensemble ResDenseNet: Alzheimer’s disease staging from brain MRI using deep weighted ensemble transfer learning

Alzheimer’s disease (AD) is a persistent neurological disorder with almost no current cure, although available medications can mitigate its progression. The timely identification and staging of AD is pivotal in impeding and managing its advancement. This study aims to develop a comprehensive framework for the early detection of AD and the classification of medical images across four AD stages. We have proposed a weighted ensemble deep transfer learning framework using two pretrained CNN architectures, namely ResNet152V2 and DenseNet201. Additionally, gradient-weighted class activation mapping (Grad-CAM) is utilized to enhance interpretability by capturing gradients associated with AD in brain MRI images, propagating them to the final convolutional layer. This technique narrows the interpretability gap in deep learning models, enhancing accessibility and understanding of their decision-making processes, particularly in the context of AD diagnosis. Two different MRI datasets have been used to train and evaluate the performances of the proposed framework focusing on generalization capability with diverse data. The performances of the framework are analyzed meticulously under different case studies. The experimental results demonstrate that the weighted ensemble architecture exhibits superior performance characteristics over base models showing accuracy of 98%, which is compatible with related state of the art algorithms.

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

Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture.

Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help prevent the progression to severe stages of the disease. In the existing literature, diagnostic test scores and clinical status have been provided for specific time points, and predicting the disease progression poses a significant challenge. However, few studies focus on longitudinal data to build deep-learning models for AD detection. These models are not stable to be relied upon in real medical settings due to a lack of adaptive training and testing. We aim to predict the individual's diagnostic status for the next six years in an adaptive manner where prediction performance improves with the number of patient visits. This study presents a Sequence-Length Adaptive Encoder-Decoder Long Short-Term Memory (SLA-ED LSTM) deep-learning model on longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative archive. In the suggested approach, decoder LSTM dynamically adjusts to accommodate variations in training sequence length and inference length rather than being constrained to a fixed length. We evaluated the model performance for various sequence lengths and found that for inference length one, sequence length nine gives the highest average test accuracy and area under the receiver operating characteristic curves of 0.920 and 0.982, respectively. This insight suggests that data from nine visits effectively captures meaningful cognitive status changes and is adequate for accurate model training. We conducted a comparative analysis of the proposed model against state-of-the-art methods, revealing a significant improvement in disease progression prediction over the previous methods.

Electrochemical cDNA-sensing of miRNA-137 for detection of Alzheimer's disease using CuO and PEI deposited Au-Pd bimetallic nanoparticles

Alzheimer's disease (AD) is the third most predominantly occurring disease worldwide that is characterized by progressive deterioration of brain cells and change in behaviour, personality, orientation of time and space, leading to functional disability which affects the daily life of patient. We report an electrochemical complementary DNA (cDNA)-sensor for detection of AD by using blood biomarker, micro-ribonucleic acid-137 (miRNA-137). In this context, the gold-palladium/polyethyleneimine@copperoxide nanocomposite-coated fluorine tin oxide (Au-Pd/PEI@CuO/FTO) electrode is used as an immobilization platform for complementary DNA (cDNA) against the target miRNA-137. The thiolated-cDNA (Thi-cDNA) and Au-Pd bimetallic particles interact through the stable electrostatic binding. The fabricated Au-Pd/PEI@CuO/FTO was characterized by field emission scanning electron microscopy, cyclic voltammetry, and electrochemical Impendence Spectroscopy. The sensor showed response time of 20 min with the linear range of 1fM to 100 nM and limit of detection as 0.164pM. The recovery of the cDNA-sensor for spiked human serum sample ranged from 99.3 % to 103.28 % with RSD value of 0.62 % to 2.81 %. The easy-to-use procedure, cost effectiveness, compactness, long shelf life (28 days) and high selectivity of cDNA-sensor towards miRNA-137 over even the one-base pair mismatch recommends the sensor's portability and real-life usability for AD detection.

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.

Diagnostic performance of plasma pTau217, pTau181, Aβ1-42 and Aβ1-40 in the LUMIPULSE automated platform for the detection of Alzheimer disease

BackgroundRecently developed blood markers for Alzheimer's disease (AD) detection have high accuracy but usually require ultra-sensitive analytic tools not commonly available in clinical laboratories, and their performance in clinical practice is unknown.MethodsWe analyzed plasma samples from 290 consecutive participants that underwent lumbar puncture in routine clinical practice in a specialized memory clinic (66 cognitively unimpaired, 130 participants with mild cognitive impairment, and 94 with dementia). Participants were classified as amyloid positive (A +) or negative (A-) according to CSF Aβ1–42/Aβ1–40 ratio. Plasma pTau217, pTau181, Aβ1–42 and Aβ1–40 were measured in the fully-automated LUMIPULSE platform. We used linear regression to compare plasma biomarkers concentrations between A + and A- groups, evaluated Spearman’s correlation between plasma and CSF and performed ROC analyses to assess their diagnostic accuracy to detect brain amyloidosis as determined by CSF Aβ1–42/Aβ1–40 ratio. We analyzed the concordance of pTau217 with CSF amyloidosis.ResultsPlasma pTau217 and pTau181 concentration were higher in A + than A- while the plasma Aβ1–42/Aβ1–40 ratio was lower in A + compared to A-. pTau181 and the Aβ1–42/Aβ1–40 ratio showed moderate correlation between plasma and CSF (Rho = 0.66 and 0.69, respectively). The areas under the ROC curve to discriminate A + from A- participants were 0.94 (95% CI 0.92–0.97) for pTau217, and 0.88 (95% CI 0.84–0.92) for both pTau181 and Aβ1–42/Aβ1–40. Chronic kidney disease (CKD) was related to increased plasma biomarker concentrations, but ratios were less affected. Plasma pTau217 had the highest fold change (× 3.2) and showed high predictive capability in discriminating A + from A-, having 4–7% misclassification rate. The global accuracy of plasma pTau217 using a two-threshold approach was robust in symptomatic groups, exceeding 90%.ConclusionThe evaluation of blood biomarkers on an automated platform exhibited high diagnostic accuracy for AD pathophysiology, and pTau217 showed excellent diagnostic accuracy to identify participants with AD in a consecutive sample representing the routine clinical practice in a specialized memory unit.

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.

Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.

This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on individuals and society, emphasizing the crucial need for timely diagnosis. We present a multistage convolutional neural network (CNN)-based framework for AD detection and sub-classification using brain magnetic resonance imaging (MRI). After preprocessing, a 26-layer CNN model was designed to differentiate between healthy individuals and patients with dementia. After detecting dementia, the 26-layer CNN model was reutilized using the concept of transfer learning to further subclassify dementia into mild, moderate, and severe dementia. Leveraging the frozen weights of the developed CNN on correlated medical images facilitated the transfer learning process for sub-classifying dementia classes. An online AD dataset is used to verify the performance of the proposed multistage CNN-based framework. The proposed approach yielded a noteworthy accuracy of 98.24% in identifying dementia classes, whereas it achieved 99.70% accuracy in dementia subclassification. Another dataset was used to further validate the proposed framework, resulting in 100% performance. Comparative evaluations against pre-trained models and the current literature were also conducted, highlighting the usefulness and superiority of the proposed framework and presenting it as a robust and effective AD detection and subclassification method.