Dementia

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification. The anomaly detection e...

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.

BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or t...

Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus.

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerab...

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detec...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of...

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decl...

Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar thr...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This stu...

3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer's disease via machine learning.

Alzheimer's Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation ...

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.

BACKGROUND: To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currentl...

An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease.

Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of early diagnosis. Based on intrinsically related subst...

Erratum to "Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning".

[This corrects the article DOI: 10.1177/25424823241289804.].

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD...

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks.

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and develop...

Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface.

Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progressio...

Cholinesterase Inhibitory Activity of Paeoniflorin: Molecular Dynamics Simulation, and In Vitro Mechanistic Investigation.

Alzheimer's disease (AD), a neurological disorder, is one of the major reasons for memory loss in the world. AD is characterized by a sequela of cogni...

Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine t...

MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.

BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as ...

Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials.

Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected...