Multimodal adaptive fusion deep analysis model for Alzheimer's disease exploration and diagnosis.

Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage. The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis. This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes. Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology. Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been receiving increasing attention. However, the presence of numerous redundant features and corrupted neuroimages within multimodal datasets poses significant challenges for existing methods. In this paper, we propose a feature selection method named Enhanced Multimodal Low-rank Embedding (EMLE) for multimodal AD diagnosis. Unlike previous methods utilizing convex relaxations of the -norm, EMLE exploits an -norm regularized projection matrix to obtain an embedding representation and select informative features jointly for each modality. The -norm, employing an upper-bounded nonconvex Minimax Concave Penalty (MCP) function to characterize sparsity, offers a superior approximation for the -norm compared to other convex relaxations. Next, a similarity graph is learned based on the self-expressiveness property to increase the robustness to corrupted data. As the approximation coefficient vectors of samples from the same class should be highly correlated, an MCP function introduced norm, i.e., matrix -norm, is applied to constrain the rank of the graph. Furthermore, recognizing that diverse modalities should share an underlying structure related to AD, we establish a consensus graph for all modalities to unveil intrinsic structures across multiple modalities. Finally, we fuse the embedding representations of all modalities into the label space to incorporate supervisory information. The results of extensive experiments on the Alzheimer's Disease Neuroimaging Initiative datasets verify the discriminability of the features selected by EMLE.

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis

Alzheimer's disease is the most common age-related neurodegenerative disorder and its burden on patients, families, and society grows significantly with lifespan. Early modifications of risk-enhancing lifestyles and treatment initiation expand personal autonomy and reduce management costs. Many clinical trials with potentially disease-modifying drugs are devoted to mild cognitive impairment (MCI) prodromal-to-Alzheimer's disease. The identification of biomarkers for early diagnosis may thus be crucial for early intervention and identification of high-risk subjects, the most appropriate target of new drugs as soon as they will be discovered. INTERCEPTOR is a strategic project by the Italian Ministry of Health and the Italian Medicines Agency (AIFA), aiming to validate the best combination (highly accurate, non-invasive, available on the whole national territory and financially sustainable) of biomarkers and organizational model for early diagnosis. 500 MCI subjects will be enrolled at baseline and followed-up for 3 years for at least 400 of them in order to define a "hub & spoke" nationwide model with recruiting (spokes) centers for MCI identification and expert (hubs) centers for risk diagnosis.

Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis.

The role of positron emission tomography in the diagnosis of Alzheimer's disease.

Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.

Alzheimer’s disease diagnosis via multimodal feature fusion

Battle against Alzheimer's Disease: The Scope and Potential Value of Magnetic Resonance Imaging Biomarkers

Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease

ObjectivesThere is a lack of studies on the rate and temporal changes of infections in relation to Alzheimer's disease (AD) diagnosis. We studied the infection rate in persons with and without AD yearly 5 years before and after AD diagnosis. DesignRegister-based cohort study. Setting and ParticipantsWe used the Medication Use and Alzheimer's Disease cohort with 70,718 Finnish community dwellers diagnosed with AD between 2005 and 2011 and an equal number of age, sex- and region-of-residence–matched comparison persons. MethodsData on comorbidities, medication use, and hospital days due to infection were retrieved from multiple nationwide registers. The rate of hospitalization and accrued hospital days due to infections were calculated yearly during the follow-up. The accumulation of hospital days was investigated with the negative binomial model. ResultsDuring the follow-up, one-half of persons with AD had inpatient stays due to infections compared with 34% of persons without AD. The infection rate increased substantially 1 to 2 years before AD diagnosis. At AD diagnosis, the rate of inpatient stays and outpatient visits due to infection was higher (15 per 100 person-years) in persons with AD than in comparison persons (9 per 100 person-years), and the accumulation of hospital days in persons with AD was higher a year after the diagnosis (incidence rate ratio, 1.21; 95% CI, 1.11-1.32) due to higher infection rate. The most common infection diagnoses in both groups were pneumonia and genitourinary infections. Conclusions and ImplicationsCompared with matched comparison persons, the higher hospitalization rate due to infections could be caused by systemic inflammation related to AD, infections generally treated in outpatient care, delirium symptoms associated with infections, and caregiver burden. The prevention of infections should be part of the care of cognitive disorders throughout the disease.

The paper presents a comprehensive study on predictive models for Alzheimer's disease (AD) and Mild cognitive impairment (MCI) diagnosis, implementing a combination of cognitive scores and artificial intelligence algorithms. The research includes detailed analyses of clinical and demographic variables such as age, education, and various cognitive and functional scores, investigating their distributions and correlations with AD and MCI. The study utilizes several machine-learning classifiers, comparing their performance through metrics like accuracy, precision, and area under the ROC curve (AUC). Key findings include the influence of gender on AD prevalence, the potential protective effect of education, and the significance of functional decline and cognitive performance scores in the models. The results demonstrate the effectiveness of ensemble methods and the robustness of the models across different data subsets, highlighting the potential of artificial intelligence in enhancing diagnostic accuracy for Alzheimer's disease and Mild cognitive impairment.

Precision medicine, also known as personalized medicine, aims to tailor medical care to individual characteristics, genetic information, and lifestyle for more accurate disease risk predictions and personalized therapies. Traditional methods in precision medicine, such as clinical assessments, laboratory testing, and pathology testing, can be enhanced with AI models to improve accuracy, precision, and personalization. Genomic analysis, disease prediction, drug discovery, and imaging analysis are key components of precision medicine. Wearable devices support continuous monitoring for proactive intervention. ML algorithms like random forest and K-means clustering are used for prediction and early diagnosis of heart disease. A deep learning model for Alzheimer's disease diagnosis and a recommended application for maintaining health details are also suggested. Recursive feature elimination is used in disease prediction and treatment policy for diabetes.

AD-LDB: A Modality-Incomplete Learning Model for Alzheimer's Disease Diagnosis

Trends in health service use and potentially avoidable hospitalizations before Alzheimer's disease diagnosis: A matched, retrospective study of US Medicare beneficiaries