用神经符号系统融合脑影像与临床数据，提升阿尔茨海默病诊断可解释性。
NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis
- 神经网络分析MRI，大语言模型提取医学规则指导符号推理
- 在ADNI数据集上准确率提升2.91%，F1-score提高3.43%
- 适合需要高可解释性的医疗AI研究与临床辅助决策场景
阿尔茨海默病（AD）诊断复杂，需整合脑部影像与临床数据。尽管深度学习在脑部MRI分析中表现良好，但常作为黑箱，难以解释，且缺乏有效整合生物标志物、病史和人口统计信息等关键临床数据的机制。为此，我们提出NeuroSymAD，一种神经符号框架，将神经网络与符号推理相结合。神经网络感知脑部MRI扫描，大语言模型（LLM）提炼医学规则，引导符号系统对生物标志物和病史进行推理。该结构化集成提升了诊断准确率与可解释性。在ADNI数据集上的实验表明，NeuroSymAD相较于现有方法，准确率最高提升2.91%，F1-score最高提升3.43%，并提供透明可解释的诊断结果。
原文摘要 · Abstract (English)
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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