Alzheimer's Disease

Robust brain age estimation from structural MRI with contrastive learning

Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scalable and robust alternative to supervised approaches for brain age estimation. We introduce a novel contrastive loss function, $\mathcal{L}^{exp}$, and evaluate it across multiple public neuroimaging datasets comprisin...

Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans

Vision Transformers (ViTs) have gained significant popularity in the natural image domain but have been less successful in 3D medical image segmentation. Nevertheless, 3D ViTs are particularly interesting for large medical imaging volumes due to their efficient self-supervised training within the masked autoencoder (MAE) framework, which enables the use of imaging data without the need for expen...

Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care

The recent boom of large language models (LLMs) has re-ignited the hope that artificial intelligence (AI) systems could aid medical diagnosis. Yet d...

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

INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detection tasks in brain images from curated, high-qualit...

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

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 Alzheimer's Disease-Related Dementias (ADRD) mortal...

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 learning models based on molecular fingerprints," empl...

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 decline, necessitating early diagnosis for effective...

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, it is still unclear how these abnormalities relat...

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 patients demonstrating twice the dementia incidence rate ...

Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning

Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narr...

Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks

The global prevalence of dementia is projected to double by 2050, highlighting the urgent need for scalable diagnostic tools. This study utilizes di...

X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis

Interpretable models are crucial for supporting clinical decision-making, driving advances in their development and application for medical images. ...

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers

Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that p...

DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI

Accurate diagnosis of brain disorders such as Alzheimer's disease and brain tumors remains a critical challenge in medical imaging. Conventional met...

Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model

Early diagnosis of Alzheimer's Disease (AD), especially at the mild cognitive impairment (MCI) stage, is vital yet hindered by subjective assessment...

Effective Red-Teaming of Policy-Adherent Agents

Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. The challeng...

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 intelligence (AI) to better understanding of aging and ...

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 older adults. However, associations between sedenta...

Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking

Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre...