CTG-FRAME: development and validation of a multi‑modal deep learning for intrapartum escalation support and severe fetal acidemia prediction.

Abstract

Background: Cardiotocography is important for continuous intrapartum fetal surveillance, yet its clinical effectiveness is limited by high inter-observer variability and a false-positive burden of approximately 60%. These limitations contribute to unnecessary intervention while delaying recognition of fetal acidemia. For clinical use, decision-support systems must not only achieve high accuracy but also define their role in guiding escalation of care.

Methods: We developed and evaluated CTG-FRAME (CardioTocoGraphy-Fetal Risk Assessment and Monitoring Engine), an end-to-end deep learning decision-support framework for identifying risk of severe fetal acidemia (umbilical arterial pH < 7.05) using fetal heart rate and uterine contraction signals. The framework integrates self-supervised momentum-contrast pre-training, a hybrid ResNet-temporal convolutional encoder, bidirectional cross-attention, and a Transformer to capture global temporal context. The model was trained and internally validated on the CTU-UHB dataset (n = 552; ~8% acidemia) using three-fold cross-validation. External transferability and calibration were assessed in a separate multicentre cohort (SPaM; n = 300; ~20% acidemia) not used during model development. CTG-FRAME outputs a probability of acidemia, estimates continuous pH severity, and identifies signal segments contributing to each prediction. Training employed a difficulty-aware multi-stage curriculum to address severe class imbalance. Model performance was evaluated using AUROC, sensitivity, specificity, and agreement measures.

Results: On CTU-UHB, the model achieved an AUROC of 0.888, with sensitivity 0.850 and specificity 0.964 at the predefined operating threshold (≈0.52). External evaluation of SPaM showed predicted abnormality rates consistent with cohort prevalence without re-tuning, indicating calibration preservation across sites. Ablation analysis demonstrated that the staged curriculum was necessary to prevent collapse to majority-class prediction, and that the self-supervised pre-training substantially improved pH estimation accuracy.

Conclusions: CTG-FRAME is designed to facilitate the escalation of clinical review rather than autonomous clinical action by defining a clear decision-analytic operating region and generating interpretable, context-linked evidence. This work advances obstetric artificial intelligence by making clinical intent, operating trade-offs, and workflow roles explicit, moving beyond accuracy toward verifiable bedside utility.