Cardiotocography (CTG) is a doppler ultrasound–based technique used during pregnancy and labor to monitor fetal well-being by recording fetal heart rate (FHR) and uterine contractions (UC). CTG can be done continuously or intermittently, with leads placed either externally or internally. External CTG involves the use of two sensors placed on the birthing parent’s belly: an ultrasound transducer placed above the fetal heart position to monitor FHR, and a tocodynamometer (pressure sensor) placed on the fundus of the uterus to measure UC.
Currently, providers interpret CTG recordings using guidelines like those from the National Institute of Child Health and Human Development (NICHD; guidelines) or the International Federation of Gynecologists and Obstetricians (FIGO; guidelines). These standards define different patterns in the CTG and FHR traces that may indicate fetal distress.
Today we present work from our recent paper, ”Development and evaluation of deep learning models for cardiotocography interpretation”, in which we describe research on our new machine learning (ML) model that will provide objective interpretation assistance to health providers to reduce burden and potentially improve fetal outcomes. Using an open-source CTG dataset, we develop end-to-end neural network-based models to predict measures of fetal well-being, including both objective (fetal arterial cord blood pH, i.e., fetal acidosis) and subjective (fetal Apgar scores) measures. Given the potential high stakes nature of the use-case if utilized in a clinical setting, we perform extensive evaluations to examine how the model performs with varying inputs, including FHR only, FHR+UC, and FHR+UC+Metadata.