A Foundation Model Approach for Fetal Stress Prediction During Labor From cardiotocography (CTG) recordings

TL;DR

This paper introduces a self-supervised deep learning method using transformer architecture for fetal stress prediction from CTG recordings, significantly improving accuracy and addressing data scarcity issues.

Contribution

It is the first to apply self-supervised pre-training to intrapartum CTG analysis, leveraging large unlabeled datasets to enhance predictive performance.

Findings

Achieved AUC of 0.83 on the test set, surpassing previous benchmarks.

Demonstrated clinically meaningful predictions even with normal umbilical pH.

Provided reproducible dataset splits and model weights for benchmarking.

Abstract

Intrapartum cardiotocography (CTG) is widely used for fetal monitoring during labor, yet its interpretation suffers from high inter-observer variability and limited predictive accuracy. Deep learning approaches have been constrained by the scarcity of CTG recordings with clinical outcome labels. We present the first application of self-supervised pre-training to intrapartum CTG analysis, leveraging 2,444 hours of unlabeled recordings for masked pre-training followed by fine-tuning on the 552-recording CTU-UHB benchmark. Using a PatchTST transformer architecture with a channel-asymmetric masking scheme designed for fetal heart rate reconstruction, we achieve an area under the receiver operating characteristic curve of 0.83 on the full test set and 0.853 on uncomplicated vaginal deliveries, exceeding previously reported results on this benchmark (0.68-0.75). Error analysis reveals that false-positive alerts typically correspond to CTG patterns judged concerning on retrospective clinical review, suggesting clinically meaningful predictions even when umbilical pH is normal. We release standardized dataset splits and model weights to enable reproducible benchmarking. Our results demonstrate that self-supervised pre-training can address data scarcity in fetal monitoring, offering a path toward reliable decision support in the labor room.