用AI增强心电图筛查查加斯病，结合临床证据提升诊断可信度
CardioRAG: A Retrieval-Augmented Generation Framework for Multimodal Chagas Disease Detection
- 融合大语言模型与心电图特征，实现可解释的多模态诊断
- 召回率达89.80%，F1最高达0.68，有效识别需优先检测病例
- 适合医疗资源有限地区使用，推动可信医学AI落地
查加斯病全球影响近600万人，其中查加斯心肌病为最严重并发症。在血清学检测能力不足的地区，基于人工智能的心电图（ECG）筛查成为关键替代方案。然而现有机器学习方法存在准确率低、依赖大规模标注数据、且未能充分融入循证临床诊断指标等问题。本文提出心血管检索增强生成框架CardioRAG，将大语言模型与可解释的ECG临床特征（如右束支传导阻滞、左前分支阻滞、心率变异性指标）相结合。该框架利用变分自编码器学习的语义表示进行病例检索，提供上下文参考以辅助临床推理。评估显示，其召回率达89.80%，最大F1分数为0.68，能有效识别需优先进行血清学检测的阳性病例。CardioRAG提供了一种可解释、基于临床证据的方法，特别适用于资源匮乏地区，为嵌入临床指标的可信医学AI系统提供了可行路径。
原文摘要 · Abstract (English)
Chagas disease affects nearly 6 million people worldwide, with Chagas cardiomyopathy representing its most severe complication. In regions where serological testing capacity is limited, AI-enhanced electrocardiogram (ECG) screening provides a critical diagnostic alternative. However, existing machine learning approaches face challenges such as limited accuracy, reliance on large labeled datasets, and more importantly, weak integration with evidence-based clinical diagnostic indicators. We propose a retrieval-augmented generation framework, CardioRAG, integrating large language models with interpretable ECG-based clinical features, including right bundle branch block, left anterior fascicular block, and heart rate variability metrics. The framework uses variational autoencoder-learned representations for semantic case retrieval, providing contextual cases to guide clinical reasoning. Evaluation demonstrated high recall performance of 89.80%, with a maximum F1 score of 0.68 for effective identification of positive cases requiring prioritized serological testing. CardioRAG provides an interpretable, clinical evidence-based approach particularly valuable for resource-limited settings, demonstrating a pathway for embedding clinical indicators into trustworthy medical AI systems.
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