用多智能体LLM解析胎心监护数据，结果透明可懂
CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
- 分五个医学特征由专用智能体分析，再汇总判断胎儿健康
- 在神经胎儿数据集上达96.4%准确率和97.8%F1分数
- 适合临床辅助诊断与需要可解释性的医疗场景
远程胎监技术日益普及，但现有系统解释性差，使孕妇难以理解原始胎心率（FHR）与宫缩（UC）数据。本文提出CTG-Insight，一个基于多智能体的LLM框架，对胎儿心率与宫缩信号进行结构化解读。依据医学指南，将每条CTG波形分解为五类医学特征：基线、变异度、加速、减速及正弦波模式，每类由专用智能体分析，最终聚合智能体生成综合分类结果并附自然语言解释。在NeuroFetalNet数据集上的评估显示，该框架达到96.4%准确率与97.8% F1-score，优于深度学习模型与单智能体基线。本工作贡献了一个可解释且可扩展的胎心监护分析框架。
原文摘要 · Abstract (English)
Remote fetal monitoring technologies are becoming increasingly common. Yet, most current systems offer limited interpretability, leaving expectant parents with raw cardiotocography (CTG) data that is difficult to understand. In this work, we present CTG-Insight, a multi-agent LLM system that provides structured interpretations of fetal heart rate (FHR) and uterine contraction (UC) signals. Drawing from established medical guidelines, CTG-Insight decomposes each CTG trace into five medically defined features: baseline, variability, accelerations, decelerations, and sinusoidal pattern, each analyzed by a dedicated agent. A final aggregation agent synthesizes the outputs to deliver a holistic classification of fetal health, accompanied by a natural language explanation. We evaluate CTG-Insight on the NeuroFetalNet Dataset and compare it against deep learning models and the single-agent LLM baseline. Results show that CTG-Insight achieves state-of-the-art accuracy (96.4%) and F1-score (97.8%) while producing transparent and interpretable outputs. This work contributes an interpretable and extensible CTG analysis framework.
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