A new UCLA study reveals Alzheimer’s may not strike randomly, and early warning signs may be hiding in your medical history.

Researchers have identified sequential diagnostic patterns that may enhance early detection and improve strategies for disease prevention. Researchers at UCLA Health have uncovered four distinct ...

The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by ...

A single, freely available, noninvasive brain scan done in just a few minutes during midlife can predict what chronic ...

Eli Lilly and Company is conducting a study titled ‘A Study of Remternetug Versus Placebo in Early Alzheimer’s Disease Participants at Risk for Cognitive and Functional Decline.’ The study ...

This article investigates machine learning (ML) techniques for the early diagnosis of acute neurological diseases, especially Alzheimer’s disease (AD). A machine-learning-based classification method ...

Alzheimer's disease is known to affect the brain in stages. While amyloid plaques often build up early, tau tangles are more closely tied to memory loss and other symptoms.

Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might slow down the disease's progression. This problem is commonly posed as a classification task performed by ...

Alzheimer’s disease (AD) progresses relentlessly from the preclinical to the dementia stage. The process begins decades before the diagnosis of dementia. Therefore, it is crucial to detect early ...

This repository contains a comprehensive deep learning solution for Alzheimer's Disease Classification using state-of-the-art DenseNet architectures optimized with Optuna hyperparameter tuning. The ...

These findings emphasize the need for stricter PFAS regulation and highlight the potential of machine learning in guiding prevention strategies. Keywords: chronic obstructive pulmonary disease, ...