Alzheimer's Disease

Brain age prediction from MRI scans in neurodegenerative diseases.

PURPOSE OF REVIEW: This review explores the use of brain age estimation from MRI scans as a biomarke...

Artificial Intelligence-Assisted Hippocampal Segmentation and Its Diagnostic Value for Alzheimer's Disease: A Meta-analysis.

BACKGROUND: Hippocampal atrophy is a key marker of Alzheimer's disease (AD) and mild cognitive impai...

An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.

BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring...

Differential dementia detection from multimodal brain images in a real-world dataset.

INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detecti...

Correction to "Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data".

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Although there are notable structural abnormalities in the brain associated with psychotic diseases,...

Non-traditional socio-environmental and geospatial determinants of Alzheimer's disease-related dementia mortality.

IMPORTANCE: Recent data point to the impact of non-traditional environmental and social factors on A...

Relational Bi-level aggregation graph convolutional network with dynamic graph learning and puzzle optimization for Alzheimer's classification.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive de...

Letter to the Editor regarding "Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints" by Song et al. (2024), Sci. Total Environ. 950 175091.

Song et al. (2024), "Prediction of PFAS bioaccumulation in different plant tissues with machine lear...

Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study.

BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patient...

Nonlinear Associations of Accelerometer-Based Sedentary Time With Cognitive Functions in the UK Biobank.

OBJECTIVES: Device-based sedentary time shows a nonlinear association with incident dementia among o...

Toward AI-Driven Precision Measurement of Cognition, Behavior, and Psychological Function in Aging and Alzheimer's Disease and Alzheimer's Disease-Related Dementias.

The National Institute on Aging (NIA) is at the forefront of leveraging advances in artificial intel...

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical se...

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis.

Positron emission tomography (PET) imaging technology is widely used for diagnosing Alzheimer's dise...

Detection of emergency department patients at risk of dementia through artificial intelligence.

INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (E...

Neural Metabolic Networks: Key Elements of Healthy Brain Function.

Neural networks are responsible for processing sensory stimuli and driving the synaptic activity req...

Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.

There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and acc...

Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer's disease.

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma protei...

ML-Driven Alzheimer's disease prediction: A deep ensemble modeling approach.

Alzheimer's disease (AD) is a progressive neurological disorder characterized by cognitive decline d...

Deep-Diffeomorphic Networks for Conditional Brain Templates.

Deformable brain templates are an important tool in many neuroimaging analyses. Conditional template...

Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis.

BACKGROUND: Considering the prevalence of Alzheimer's Disease (AD) among the aging population and th...