Dementia

Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.

Predicting the likelihood of developing Alzheimer's disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal br...

Advancing brain age estimation: normative deviation mapping (NDM) for sensitive detection of pathological aging.

Brain age is a valuable neuroimaging-based biomarker for assessing brain health, typically estimated using machine learning (ML) models. However, ML approaches suffer from inherent bias, requiring post-hoc correction, and may mask age-related biological variation, limiting their sensitivity to detect subtle biological aging. To overcome these limitations, we proposed a normative deviation mapping ...

Causal inference in real-world dementia research: a systematic review protocol.

BACKGROUND: Dementia presents complex challenges for causal inference due to its multifactorial aetiology and slow, heterogeneous progression. Randomi...

NeuroBACE-ML: A reliability-aware screening framework for high-throughput prioritization of potent BACE1 inhibitors.

Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a key enzyme in amyloid-β generation and remains an important target in Alzheimer's d...

SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease.

Given the unclear pathogenesis and insidious progression of Alzheimer's disease (AD), the aim of the present study was to identify reliable diagnostic...

Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary preven...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twof...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...

The association between PM2.5 components and cognitive decline: the impact of measurement error correction.

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may accelerate cognitive decline and increase dementia risk, but the roles ...

MAISNet: A Multi-Species Integrated Graph Neural Network for Acetylcholinesterase Inhibitor Screening.

MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as alzheimer's disease (AD). Although existing dru...

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

OBJECTIVES: Neuropsychological (NP) tests are multi-domain in execution. Reliance on a single score representing specific domains obscures the detecti...

Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.

Dementia, which refers to disorders related to human memory, significantly affects the human brain, and a person with it can experience certain diffic...

Automated Imaging Differentiation for Dementia: Including Alzheimer Disease Dementia and Dementia with Lewy Bodies.

BACKGROUND AND OBJECTIVES: Differentiation of Alzheimer's disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water i...

Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.

BACKGROUND: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discha...

Bridging Trials and Real Life in Fixed-Duration BTKi-Venetoclax for CLL: A Delphi-Enhanced Synthesis Incorporating Artificial Intelligence (AI) Benchmarks.

BACKGROUND: Optimal patient selection for the most effective BTK inhibitor (BTKi) partner of venetoclax in fixed-duration (FD) BTKi-venetoclax regimen...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

Learning to take it personally: Precision drug repurposing through patient-specific loss on knowledge graphs using Biobank data.

OBJECTIVE: Precision medicine requires drug repurposing methods that adapt to individual patient profiles while working within regulatory frameworks. ...

Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.

OBJECTIVES: Leveraging routine electronic health records (EHR) for dementia detection is a growing field, but quality and clinical utility of existing...

Dual-model deep learning for Alzheimer's prognostication.

Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical obser...

Extended Reality in Neurosurgery : Surgical Planning, Navigation, Education, and Patient Communication.

Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), has emerged as a transformative technology i...