To develop and deploy a multimodal convolutional neural network (CNN) model that classifies dementia using structural T1 MRI scans and clinical features. This model aims to provide rapid, objective screening to augment radiologist workflow and reduce diagnostic delays.

Dementia diagnosis is often delayed, with missed cases common in primary care. Radiologists struggle with subtle, variable atrophy patterns on MRI. Successful clinical adoption requires interpretable AI that integrates into the clinical workflow, reduces interpretation time, and provides attention maps to boost confidence in screening.

We developed a multimodal framework incorporating a 3D CNN for MRI analysis and integrated clinical features (demographics, medical history, neuropsychology). Preprocessing included skull stripping, normalization, and MNI152 registration. Trained on ADNI and NACC data totaling 1,314 participants (aged 49-96), the model performs 3-way classification: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Explainability is provided via SHAP-generated MRI heatmaps and clinical importance scores.