Authors
Dian Tjondronegoro, Elizabeth Irenne Yuwono, Fabricio Da Silva Costa
Published in
BMC medical informatics and decision making. Volume 26. Issue 1. Sep 28, 2026. Epub Sep 28, 2026.
Abstract
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.
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.
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.
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.
PMID:
42806359
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.
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