Using artificial intelligence and radiomics to analyze imaging features of neurodegenerative diseases

Abstract

Introduction: Neurodegenerative diseases such as Alzheimer's and Parkinson's are characterized by complex, multifactorial progression patterns that challenge early diagnosis and personalized treatment planning. Methods: To address this, we propose an integrated AI-radiomics framework that combines symbolic reasoning, deep learning, and multi-modal feature alignment to model disease progression from structural imaging and behavioral data. The core of our method is a biologically informed architecture called NeuroSage, which incorporates radiomic features, clinical priors, and graph-based neural dynamics. We further introduce a symbolic alignment strategy (CAIS) to ensure clinical interpretability and cognitive coherence of the learned representations. Results and discussion: Experiments on multiple datasets-including ADNI, PPMI, and ABIDE for imaging, and YouTubePD and PDVD for behavioral signals-demonstrate that our approach consistently outperforms existing baselines, achieving an F1 score of 88.90 on ADNI and 85.43 on PPMI. These results highlight the framework's effectiveness in capturing disease patterns across imaging and non-imaging modalities, supporting its potential for real-world neurodegenerative disease monitoring and diagnosis.

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