Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer’s disease progression

Abstract

The diagnosis of Alzheimer’s disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the progression of Alzheimer’s disease. We propose Mul-KMPP, a Multi-Knowledge Informed Deep Learning Model for Multi-Point Prediction of AD progression, intended to facilitate precise assessments of AD progression in older adults. Firstly, we designed a dual-path methodology to capture global and local brain characteristics for visual feature extraction (utilizing MRIs). Then, we developed a diagnostic module before the prediction module, leveraging AAL (Anatomical Automatic Labeling) knowledge. Following this, predictions are informed by clinical insights. For this purpose, we devised a new composite loss function, including diagnosis loss, prediction loss, and consistency loss of the two modules. To validate our model, we compiled a dataset comprising 819 samples and the results demonstrate that our Mul-KMPP model achieved an accuracy of 86.8%, sensitivity of 86.1%, specificity of 92.1%, and area under the curve (AUC) of 95.9%, significantly outperforming several competing diagnostic methods at every time point. The source code for our model is available at https://github.com/Camelus-to/Mul-KMPP.