Deep Learning-based Synthesis of Amyloid PET Images from Structural MRI in Alzheimer's Disease.

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

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...

Neuro-AI: An Integrated Deep Learning Framework for Enhanced MRI-Based Alzheimer’s Disease Classification

Alzheimer’s Disease (AD) is a progressive neurodegenerative condition wherein the early identification of Cognitively Normal (CN), Mild Cognitive Impairment (MCI) and AD is of importance to researchers. The different medical equipment used and data collection settings bring about differences that do not pertain biologi...

Hybrid CNN-Transformer Framework for Molecular Imaging-based Early Alzheimer's Disease Detection.

Raw registered PET slices did not provide stable participant-generalizable classification of an SUVR-derived amyloid target, and participant-level separation, validated tracer quantification, demographic baselines, and external validation remain necessary before clinical interpretation.

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...

A Deep Learning-Powered Framework for the Early Prediction of Alzheimer's Disease through Advanced Magnetic Resonance Imaging and Analysis.

An advanced DL-powered framework for the early prediction of AD using MRI data demonstrates high accuracy, robustness, and interpretability, supporting its potential for reliable early AD diagnosis in clinical settings.