Researchers propose MVMGNN, a multi-view masked graph neural network for Alzheimer's disease diagnosis using structural MRI, which outperforms competing approaches on the ADNI dataset by jointly captu…
A new AI model developed using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to detect Alzheimer's disease early by handling class imbalance and refining feature selection. The…
Researchers propose ProMUSE, a progressive multi-modal uncertainty-guided network for Alzheimer's disease classification that adaptively determines when additional imaging modalities are needed, reduc…
Researchers propose Artemis, a region-level causal framework for multimodal neuroimaging that adjusts for demographic confounders like age and sex at each brain region independently. The method improv…
An arXiv preprint (arXiv:2606.03995) reports an explainable machine learning model that achieved near-perfect three-class classification of Alzheimer's disease, mild cognitive impairment, and normal c…
Researchers developed an XGBoost machine learning model using eight routine clinical features from the Alzheimer's Disease Neuroimaging Initiative dataset that achieved 94.3% accuracy and a 0.982 macr…