In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable.
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
The study presents an explainable multimodal deep‑learning framework that combines a 3D CNN for T1‑weighted MRI with a feedforward network for harmonized clinical and demographic data to diagnose Alzheimer’s disease. Using 6,479 ADNI records and 1,703 OASIS‑3 records, the authors compare various model configurations on three‑way and pairwise diagnostic tasks, finding that performance and explanations vary by task, modality, fusion strategy, and cohort. SHAP and Integrated Gradients consistently highlight the MMSE score as the most influential tabular feature, while CAM‑based explanations differ across model setups and cohorts, indicating that explainability is not a stable property under cohort shift.
arXiv:2512. 10966v3 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data.
arXiv:2606. 11794v1 Announce Type: cross Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical staging remains time-intensive and prone to variability.
arXiv:2605.28397v2 Announce Type: replace Abstract: Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied...
arXiv:2607. 01401v1 Announce Type: cross Abstract: INTRODUCTION: Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challenging because disease-related structural changes are often subtle and heterogeneous.
arXiv:2606. 17867v1 Announce Type: cross Abstract: Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization -- the relationships among these modalities remain poorly understood.
MINT (Multimodal Imaging-to-Speech Knowledge Transfer) is a three-stage framework that transfers MRI-derived biomarkers to speech representations for early Alzheimer’s screening. An MRI teacher creates a compact embedding space for CN‑versus‑MCI classification, and a residual projection head aligns speech features to this space using a geometric loss, allowing imaging‑free inference. Experiments on ADNI‑4 show that aligned speech matches speech baselines, while multimodal fusion outperforms MRI alone, and ablations highlight dropout regularization and self‑supervised pretraining as key design choices.
arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
arXiv:2606. 09671v1 Announce Type: cross Abstract: Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring.
The paper introduces a two-stage multi‑modal MRI framework that processes different MRI modalities independently before integrating them through late fusion. First, the model estimates a probability distribution over six developmental stages, then it predicts age using probability‑weighted, stage‑specialized experts. Experiments across nine datasets—from fetal to elderly—show the method outperforms existing baselines, reducing mean absolute error by 13% and 78% in in‑domain and out‑of‑domain settings, and multi‑modal integration yields 12‑13% performance gains. Analysis on ADNI clinical groups indicates that the predicted brain age gap could help characterize Alzheimer’s‑related brain aging.