Binary classification of 18 F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI

Novel PET-driven brain mapping for diagnosis of Alzheimer’s disease: integrated feature extraction and ranking capabilities

Early diagnosis of Alzheimer’s disease (AD) remains a significant challenge in modern healthcare. Positron emission tomography (PET) imaging combined with machine learning offers a powerful framework for this task, where brain atlases play a critical role in feature extraction. Although predefined atlases are widely us...

Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum.

Weakly Supervised MRI-Based Classification of Alzheimer’s Disease Using Clinical Pseudo-Labels

Alzheimer’s disease (AD) classification from structural magnetic resonance imaging (MRI) may benefit from weak supervision that uses clinically meaningful but imperfect supervisory signals. We evaluated a weakly supervised framework in which a multilayer perceptron (MLP) trained on age, sex, and Mini-Mental State Exami...

Brain Amyloid Burden Mapping Using MR Fingerprinting Aided by Deep Learning.

The proposed framework enables non-invasive Aβ mapping using a clinically feasible MRI protocol and may support repeated assessment for monitoring during anti-amyloid treatment.

T1 mapping in Alzheimer’s disease: a quantitative MRI study with amyloid-PET correlation

Quantitative T1 mapping (qT1) is a magnetic resonance imaging (MRI) biomarker of brain microstructural changes; however, its application in Alzheimer disease (AD) remains limited. We compared cortico-limbic qT1 values between patients with AD and healthy controls (HCs) and examined their relationship with amyloid burde...

Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis

Objective Alzheimer's disease (AD), the most common neurodegenerative disorder, is a leading cause of cognitive impairment and dementia in older adults. This study aimed to develop an interpretable machine learning model using multimodal MRI radiomics for the diagnosis of Alzheimer's disease. Materials and methods A to...