Alzheimer's Disease Detection using Gaussian Kernel Based Fuzzy C-Means Clustering Algorithm

Alzheimer's disease(AD) is a neurodegenerative disease that progresses slowly but worsens gradually, also, the most common kinds of dementia. Clinically, the diagnosis of AD is mainly based on rating scales and neuroimaging technology which is invasive, costly and time-consuming. Other than that, the clinical pathology has become irreversible when neuroimaging characteristics appear. It is imperative to develop new noninvasive methods for early diagnosis of AD. Several studies indicated the probable association of cognitive decline with gait changes might shed light on potential features for distinction of AD. This paper aims to exploit the feasibility of gait features for early diagnosis of mild cognitive impairment(MCI) and AD by using machine learning methods. A device-free AD detection system is built, with a natural undisturbed gait collecting system and a well-performed Long Short-Term Memory(LSTM) based model, in this article. Moreover, it can serve as a simplified, non-invasive, and highly accurate clinical auxiliary tool for early diagnosis and distinction of AD. Experimental results showed a 90.48%, 92.00%, and 88.24% in accuracy, sensitivity, and specificity respectively for distinguishing AD by using the method with LSTM based model. Furthermore, the gait cycle and stride length in MCI or AD were more variable than in healthy controls through redefining and calculating the gait features with skeleton data obtained by Kinect devices. © 2021 IEEE.

This study aims to detect direct cost savings with early detection of Alzheimer's disease (AD) and related dementias. We hypothesize that earlier diagnosis of AD can reduce direct healthcare costs and unnecessary utilization. A retrospective case-control study is conducted using Optum Market Clarity Claims/EHR linked dataset on Medicare Advantage population from Jan 1 2011 to Dec 31 2020. Based on whether the patient was diagnosed with prior Mild Cognitive Impairment (MCI) before progressing to an AD diagnosis per ICD-9/10 cm diagnosis codes, AD patients are categorized into early vs. late diagnosis (for those who were identified in the MCI stage vs not). Both groups are compared on annual all-cause healthcare costs and frequency of encounters longitudinally spanning across 6 years pre-index period and 3 years post-index with AD diagnosis date as the index date. To understand the impact of early diagnosis, we estimated total cost, medical service cost and drug cost post-index within the first 3 years by risk adjustment on baseline demographics, comorbidities, procedures, lab tests, and visits prior to AD diagnosis using propensity score weighting. Early-diagnosed patients (N = 421) have a lower utilization rate of inpatient skilled nursing facility, nursing home and hospitalization than late-diagnosed patients (N = 2817) after being diagnosed with AD. We also observed average medical service cost savings in all 3 years post-index, ranging from $320 to $2556 per patient per year. Per cost allocation analysis, medical service cost savings are observed in inpatient, emergency room, inpatient SNF, urgent care, nursing home/assisted living, and observation unit. Risk adjusted cost savings with statistical significance are observed in year 3 post-index. The mean cost difference between late and early diagnoses: total cost ($15472.11), medical service ($15013.7) and medication cost ($1601.61). The study highlights opportunities for earlier diagnosis and suggests long-term healthcare savings for AD patients who have been diagnosed at MCI stage compared to patients who were directly diagnosed with AD. Further research is needed to drill down into the driver of cost savings and whether it is attributable to disease management of MCI or other complications.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that affects the elderly population. The early detection and diagnosis of AD is critical for achieving effective treatment, as it can greatly improve the patient experience. AD can be viewed through imaging techniques like MRI, PET, and SPECT, providing valuable information about structural and functional changes. These findings are important in understanding this area. However, each imaging modality offers a different perspective. This information can be better collected from several of the other modalities as well as from some others to improve accuracy and reliability in AD detection. By combining information from different imaging modalities, such as MRI, PET, DTI, and fMRI, automated multimodal medical image frameworks aim to create a fused representation that preserves the relevant features from each modality. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), among other deep learning techniques, have been prevalent in these frameworks for learning discriminative and informative features from multi-modal data. In this paper, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used for experimental analysis. The proposed work gives 98.94% of accuracy and 1.06% of error which is greater than the existing approaches.

Patients with Alzheimer's disease (AD) and Parkinson's disease (PD) develop a progressive decline of visual function. This condition aggravates overall cognitive and motor abilities, is a risk factor for developing hallucinations, and can have a significant influence on general quality of life. Visual problems are common complaints of patients with PD and AD in the early stages of the disease, but they also occur during normal aging, making it difficult to differentiate between normal and pathological conditions. In this respect, their real incidence has remained largely underestimated, and no rehabilitative approaches have been standardized. With the aim to increase awareness for ocular and visual disorders, we collected the main neurophthalmologic and orthoptic parameters, including optical coherence tomography (OCT), in six patients with a diagnosis of PD, six patients with a diagnosis of early AD, and eight control subjects in an easily assessable outpatient setting. We also evaluated the patient's ability to recognize changes in facial expression. Our study demonstrates that visual problems, including blurred vision, diplopia, reading discomfort, photophobia, and glare, are commonly reported in patients with PD and AD. Moreover, abnormal eye alignment and vergence insufficiency were documented in all patients during examination. Despite the small size of the sample, we demonstrated greater ganglion cell and retinal nerve fibers layer (RNFL) damage and a defect of facial emotion recognition in AD/PD patients with respect to a comparable group of normal elderly persons, with peculiarities depending upon the disease. Ocular defects or visual discomfort could be correctly evaluated in these patients and possibly corrected by means of lens, orthoptic exercises, and visual rehabilitation. Such a practical approach may help to ameliorate motor autonomy, reading ability, and may also reduce the risk of falls, with a positive impact in daily living activities.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

BackgroundThe link between late‐life depression and Alzheimer’s disease (AD) has long been investigated, however it is still unclear whether depression acts as a risk factor, a prodromal feature or a comorbid condition in AD. Clarifying the nature of this link has important implications in the early intervention and diagnosis of AD. The relatively recent introduction of in‐vivo brain biomarkers for AD pathology offers the chance to explore the relationship between depression and pathologically‐confirmed neurodegeneration. Here, we examined the association among objective cognitive functioning, self‐reported cognitive failures and AD pathology in patients with late‐life depression.MethodSixty‐three older adults with no formal diagnosis of cognitive impairment underwent a comprehensive cognitive assessment and Amyloid‐PET Imaging at the Imperial Memory Unit. Participants were classified into two groups, according to the presence (n=33, mean age=71.82 ±4.98) or absence (n=30, mean age=71.30 ±5.80) of late‐life depression. Late‐life depression was confirmed through validated questionnaires (GDS ≥ 6 and/or HDRS ≥ 12) and medical history review. Amyloid‐PET images were qualitatively read as amyloid positive or amyloid negative by two experienced nuclear medicine radiologists (NMRs). Equivocal scans (6%) were read by a third independent NMR. Cognitive data in the two groups were compared using non‐parametric tests.ResultsAmyloid status did not differ significantly between the two groups (p=.367), with 3 (9%) depressed and 5 (17%) non‐depressed participants reported as amyloid‐positive. Despite higher levels of self‐reported cognitive failures (p<.001), the depressed group did not differ from the non‐depressed group in a global measure of cognition (MMSE depressed: 28.09±1.69; non‐depressed: 28.93±4.66) and showed worse performance in 4 (Word List short‐delay recall, Digit Span backward and forward, Coding) of the 17 cognitive measures administered. Follow up data, currently being collected at our Unit, will add information about the long‐term trajectory of cognition in late life depression.ConclusionsIn our sample, depressive symptoms were associated with a limited number of cognitive measures but not with AD pathology. This suggests that depression seems to have a limited role in the early diagnosis of AD. Future studies should examine the role of specific features of depression and of other widespread neuropsychiatric symptoms (e.g., anxiety) in AD.

Alzheimer’s disease (AD) is a progressive neurodegenerative condition representing the most common cause of dementia and currently affects millions of people worldwide. The clinical presentation includes memory impairment, cognitive decline, and neuropsychiatric symptoms, reflecting pathological hallmarks such as β-amyloid (Aβ) plaques, neurofibrillary tangles, synaptic dysfunction, and neuroinflammation. Despite being the gold standard for detecting amyloid and tau pathologies in vivo, cerebrospinal fluid (CSF) biomarkers and positron emission tomography (PET) imaging are not widely used in the clinical setting because of invasiveness, high costs, and restricted accessibility. Recent advances in blood-based biomarkers offer a promising and minimally invasive tool for early detection, diagnosis, and monitoring of AD. Ultra-sensitive analytical platforms, including single-molecule arrays (Simoa) and immunoprecipitation-mass spectrometry, now enable reliable quantification of plasma Aβ isoforms, phosphorylated tau variants (p-Tau181, p-Tau217, p-Tau231), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). In addition, blood biomarkers reflecting oxidative stress, neuroinflammation, synaptic disruption and metabolic dysfunction are under active investigation. This narrative review synthesizes current evidence on blood-based biomarkers in AD, emphasizing their biological relevance, diagnostic accuracy, and clinical applications. Finally, we highlight forthcoming challenges, such as standardization, and future directions, including the use of artificial intelligence in precision medicine.

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

Alzheimer’s Disease (AD) is the most prevalent neurodegenerative disorder and the most common cause of Dementia. Neuroimaging analyses such as T1 weighted Magnetic Resonance Imaging, Positron Emission Tomography, and the deep learning (DL) approaches have attracted researchers for automated AD diagnosis in the early stages. Therefore, a review is required to understand DL algorithms to develop more efficient AD diagnosis methods. This paper discusses a detailed review of automated early AD diagnosis using DL methods published from 2009 to 2022. The novelties of this paper include: 1) introducing popular imaging modalities, 2) discussing early biomarkers for AD diagnosis using neuroimaging scans, 3) reviewing the popular online available datasets widely used, 4) systematically describing the various DL algorithms for accurate and early assessment of AD, 5) discussion on advantages & limitations of the DL-based model for AD diagnosis, and 6) provides an outlook toward future trends derived from our critical assessment.

BackgroundWith the global change of demographics towards an increase of the elderly population, diseases related to some form of dementia have become more common in the 21st century. Leading the hall of dementia problems is Alzheimer's Disease (AD), responsible for more than 50% of the reported cases. It is also known that early diagnosis helps to lead to a better prognosis. So, this review aims to evaluate the role of disponible neuropsychological tests (NT) on early diagnosis of AD, using the available literature, in order to determine the importance of its use.MethodAn active search was performed in the PubMed database, using the keywords “Alzheimer's disease”, “early diagnosis”, “neuropsychological” and their equivalents. All articles published from 1998 to 2024 in any language were selected. The title and abstract were analyzed, duplicate results were eliminated and all studies that had the evaluation of the use of NT on early diagnosis of AD as their objective were included. Studies that analyzed only other types of dementia or non‐psychological tests were excluded.ResultThe search yielded 77 articles, with 9 meeting the inclusion criteria. Findings reveal that various NTs show promise for early AD diagnosis, with tools like the Brain Health Assessment‐Cognitive Score and the DMS48 visual recognition memory test effectively identifying early cognitive impairment. The DMS48, in particular, highlighted that mild cognitive impairment patients with visual memory deficits face a faster cognitive decline, indicating a higher AD progression risk. The visual oddity detection task also proved effective, and combining the Mini‐Mental State Examination with other NTs, such as the Addenbrooke's Cognitive Examination, in screening was especially useful. Additionally, verbal fluency tests have shown several impairments of the early phase of AD and culturally adaptable tools like the Cross‐Cultural Neuropsychological Test Battery offered reliable assessment across diverse populations.ConclusionThe literature on the use of NT on early diagnosis of AD lacks data, no randomized clinical trial or large analytical observational study was found. However, among the information analyzed, the results suggest that combining the neuropsychological measures alongside biomarkers and informant‐based assessments enhances early AD detection and diagnostic accuracy.

Alzheimer’s disease (AD) is a progressive neurological disorder characterised by cognitive decline and memory loss. Early diagnosis is essential for effective treatment, although the complexity of the initial symptoms sometimes delays it. This review addresses the development of a deep learning-based model to aid in the early diagnosis of Alzheimer’s disease using neuroimaging data. Using MRI and PET scans from public datasets such as the Alzheimer’s Disease Neuroimaging Initiative, the proposed version makes use of convolutional neural networks (CNNs) to extract feature extraction and types by looking at brain structure in and on models associated with early Alzheimer’s ailment at the version that tries to decide the values ​​Performance is measured using key parameters along with sensitivity, specificity, accuracy, and area under the curve. The purpose is to develop a predictive tool which could help medical doctors diagnose Alzheimer’s disorder in advance and, in all likelihood, enhance the affected person’s effects via timely intervention.

Isotopic N,N-dimethyl leucine tags for absolute quantification of clusterin and apolipoprotein E in Alzheimer's disease

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Alzheimer's disease (AD) is characterized by significant brain atrophy, detectable via structural MRI. There has been less focus on cortical degeneration in subcortical regional deterioration-particularly during the transition from Mild Cognitive Impairment (MCI) to AD-which remains underexplored. This study aims to identify subcortical regions with progressive atrophy using surface-based morphometry (SBM) and evaluate their potential for early AD diagnosis. This longitudinal study collected data from MCI patients (6 months) who later progressed to Alzheimer's within 3 years, alongside healthy controls followed across four time points (6 months-3 years). Reported in line with STARD guidelines, the study aimed to evaluate model performance in distinguishing progressive MCI-to-AD from healthy controls to advance early Alzheimer's diagnosis. The study leveraged the ADNI dataset to analyse 68 subcortical regions in MCI-to-AD converters (MCI-AD) and Healthy Controls (HC) over 6 months to 3 years (i.e., at 6 months, 1 year, 2 years, and 3 years). The groups were classified beforehand using the Clinical Dementia Rating and the Mini-Mental Status Examination scores. Accordingly, three surfacebased morphometry (SBM) metrics-cortical thickness (CTh), gyrification index (GI), and sulcal depth (SD)-were evaluated in the progressive MCI group (individuals with MCI who later converted to Alzheimer's disease) as well as in healthy controls, to quantify morphological changes. Finally, the morphological data were utilized to train and test machine learning models for classification and prediction. Cortical regions exhibiting significant atrophy were identified using paired-samples ttests between 6 months and 3 years. In parallel, machine learning (ML) models were trained and tested on the same dataset to differentiate progressive MCI-to-AD cases from healthy controls across multiple time points (6 months, 1 year, 2 years, and 3 years), and subsequently to predict the progression to Alzheimer's disease. Certain evaluation metrics were considered for the classifier performance, i.e., Accuracy, F1-score, and AUC-ROC. Sub-cortical SBM metrics, particularly CTh, are sensitive to early AD-related atrophy, offering potential biomarkers for disease progression. ML models trained on these features enable accurate classification, with performance peaking near diagnosis-highlighting their utility in early intervention. SBM-derived subcortical atrophy patterns aid early AD detection and, when combined with ML, offer a scalable predictive framework. Among metrics, CTh showed the greatest decline in MCI-AD, followed by SD and GI. Progressive deterioration was observed in specific subcortical regions, accelerating near diagnosis. Classifiers achieved higher accuracy in distinguishing MCI-AD from HC at later time points, highlighting stage-specific shifts in feature importance.