A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease

AI 中文解读

阿尔茨海默病的早期诊断迎来新突破。科学家开发出一套自动化系统，能直接从患者的语音测试录音中捕捉病情线索，无需繁琐的人工转录和分析。这套系统像一位细心的“语言侦探”，通过分析说话时的用词习惯、语言流畅度等细微特征，再借助强大的推理模型梳理这些特征之间的关联，从而判断病情进展。它不仅能验证医生已知的临床经验，还能发现人工分析难以察觉的新规律。对普通人而言，这意味着未来阿尔茨海默病的筛查可能像做一次简单的语音测试一样方便，患者无需经历复杂检查就能更早发现风险，为治疗争取宝贵时间；同时也降低了医生的工作负担，让大规模早期筛查成为可能。

Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale. We introduce an automated pipeline that extracts qualitative knowledge about potential AD progression indicators directly from audio recordings of verbal fluency tests. Our method uses pretrained foundation models to process raw audio and extract clinically relevant variables to construct a Bayesian Network (BN); this BN is used to reason about the AD progression markers and infer their qualitative relationships. Our system successfully recovers known clinical knowledge and identifies novel relationships between linguistic markers.