An integrated predictive model for Alzheimer’s disease progression from cognitively normal subjects using generated MRI and interpretable AI

• Published in: Scientific reports Vol. 15; no. 1; pp. 28340 - 23
• Main Authors: Aghaei, Atefe, Moghaddam, Mohsen Ebrahimi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 692/308; Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Alzheimer’s progression prediction; Automatic ROI extraction; Brain; Brain - diagnostic imaging; Cognition; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Deep learning; Diagnosis; Disease Progression; Ensemble transfer learning; Female; Humanities and Social Sciences; Humans; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; MRI; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Prediction models; Probability; Science; Science (multidisciplinary); Transfer learning; Vit-GAN; Alzheimer’s progression prediction; Probability; Ensemble transfer learning; Automatic ROI extraction; Vit-GAN; MRI
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-13478-2

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that begins with subtle cognitive changes and advances to severe impairment. Early diagnosis is crucial for effective intervention and management. In this study, we propose an integrated framework that leverages ensemble transfer learning, generative modeling, and automatic ROI extraction techniques to predict the progression of Alzheimer’s disease from cognitively normal (CN) subjects. Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we employ a three-stage process: (1) estimating the probability of transitioning from CN to mild cognitive impairment (MCI) using ensemble transfer learning, (2) generating future MRI images using Transformer-based Generative Adversarial Network (ViT-GANs) to simulate disease progression after two years, and (3) predicting AD using a 3D convolutional neural network (CNN) with calibrated probabilities using isotonic regression and interpreting critical regions of interest (ROIs) with Gradient-weighted Class Activation Mapping (Grad-CAM). However, the proposed method has generality and may work when sufficient data for simulating brain changes after three years or more is available; in the training phase, regarding available data, brain changes after 2 years have been considered. Our approach addresses the challenge of limited longitudinal data by creating high-quality synthetic images and improving model transparency by identifying key brain regions involved in disease progression. The proposed method demonstrates high accuracy and F1-score, 0.85 and 0.86, respectively, in CN to AD prediction up to 10 years, offering a potential tool for early diagnosis and personalized intervention strategies in Alzheimer’s disease.