Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis.

ABSTRACT:

Introduction

Artificial intelligence and neuroimaging enable accurate dementia prediction, but 'black box' models can be difficult to trust. Explainable artificial intelligence (XAI) describes techniques to understand model behaviour and the influence of features, however deciding which method is most appropriate is non-trivial. Vision transformers (ViT) have also gained popularity, providing a self-explainable, alternative to traditional convolutional neural networks (CNN).

Methods

We used T1-weighted MRI to train models on two tasks: Alzheimer's disease (AD) classification (diagnosis) and predicting conversion from mild-cognitive impairment (MCI) to AD (prognosis). We compared ten XAI methods across CNN and ViT architectures.

Results

Models achieved balanced accurac

SUBMITTER: Martin SA 

PROVIDER: S-EPMC11759246 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature