The gold standard to assess whether a baby is at risk of oxygen starvation during childbirth, is monitoring continuously the fetal heart rate with cardiotocography (CTG), comprising two time series: fetal heart rate and contraction strength. The goal of monitoring is to identify babies that could benefit from an emergency operative delivery (e.g., Cesarean section), in order to prevent death or permanent brain injury. The long, dynamic and complex CTG patterns are poorly understood and known to have high false positive and false negative rates. Visual interpretation by clinicians is challenging and reliable accurate fetal monitoring in labour remains an enormous unmet medical need. Our team has acquired a uniquely large and detailed cohort of routinely collected data during labour at Oxford (all monitored births between Apr’93 and Dec’18). We have already developed a basic computerized data-driven prototype for CTG evaluation: OxSys 1.5 (Georgieva at al. 2017 “Computerized data-driven interpretation of the intrapartum cadiotocogram: a cohort study”. Acta Obstet Gynecol Scand96(7)). It works comparable to CTG evaluation by doctors in clinical practice but is based only on a few clinical and CTG features and further improvements are needed. The size of our database confers scope for substantial improvement of OxSys and we are working towards developing a much more sophisticated OxSys 2.0.
In this talk I will present our work on the first application of deep learning for the analysis of the CTG. I will demonstrate that Multimodal Convolutional Neural Networks hold potential for the prediction the newborns with compromise at birth and further work is warranted. Furthermore, I will discuss why our deep learning models are currently not suitable for the detection of certain severe fetal injuries that are part of a heterogeneous, small, and poorly understood group. We suggest that the most promising way forward are hybrid approaches to CTG interpretation in labour, in which different diagnostic models can estimate the risk for different types of fetal compromise, incorporating clinical knowledge with data-driven analyses.
