CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
This provides an interpretable solution for expectant parents and medical professionals to better understand fetal health from CTG data, though it is incremental as it builds on existing medical guidelines and LLM methods.
The authors tackled the problem of limited interpretability in remote fetal monitoring by developing CTG-Insight, a multi-agent LLM framework for cardiotocography analysis, which achieved state-of-the-art accuracy of 96.4% and F1-score of 97.8% on the NeuroFetalNet Dataset.
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.