A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provide

SUBMITTER: El-Sappagh S 

PROVIDER: S-EPMC7846613 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature