NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis

• URL: http://arxiv.org/abs/2503.00510v1
• Date: Sat, 01 Mar 2025 14:29:39 GMT
• Title: NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis
• Authors: Yexiao He, Ziyao Wang, Yuning Zhang, Tingting Dan, Tianlong Chen, Guorong Wu, Ang Li,
• Abstract summary: NeuroSymAD is a neuro-symbolic framework that synergizes neural networks with symbolic reasoning.<n>A neural network percepts brain MRI scans, while a large language model distills medical rules to guide a symbolic system in reasoning over biomarkers and medical history.
• Score: 35.4733004746959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease (AD) diagnosis is complex, requiring the integration of imaging and clinical data for accurate assessment. While deep learning has shown promise in brain MRI analysis, it often functions as a black box, limiting interpretability and lacking mechanisms to effectively integrate critical clinical data such as biomarkers, medical history, and demographic information. To bridge this gap, we propose NeuroSymAD, a neuro-symbolic framework that synergizes neural networks with symbolic reasoning. A neural network percepts brain MRI scans, while a large language model (LLM) distills medical rules to guide a symbolic system in reasoning over biomarkers and medical history. This structured integration enhances both diagnostic accuracy and explainability. Experiments on the ADNI dataset demonstrate that NeuroSymAD outperforms state-of-the-art methods by up to 2.91% in accuracy and 3.43% in F1-score while providing transparent and interpretable diagnosis.

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