The OxMat dataset: a multimodal resource for the development of
  AI-driven technologies in maternal and newborn child health
M. Jaleed Khan Ioana Duta Beth Albert William Cooke Manu Vatish Gabriel Davis Jones
The rapid advancement of Artificial Intelligence (AI) in healthcare presents
a unique opportunity for advancements in obstetric care, particularly through
the analysis of cardiotocography (CTG) for fetal monitoring. However, the
effectiveness of such technologies depends upon the availability of large,
high-quality datasets that are suitable for machine learning. This paper
introduces the Oxford Maternity (OxMat) dataset, the world's largest curated
dataset of CTGs, featuring raw time series CTG data and extensive clinical data
for both mothers and babies, which is ideally placed for machine learning. The
OxMat dataset addresses the critical gap in women's health data by providing
over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated
and reviewed since 1991. The dataset also comprises over 200 antepartum,
intrapartum and postpartum clinical variables, ensuring near-complete data for
crucial outcomes such as stillbirth and acidaemia. While this dataset also
covers the intrapartum stage, around 94% of the constituent CTGS are
antepartum. This allows for a unique focus on the underserved antepartum
period, in which early detection of at-risk fetuses can significantly improve
health outcomes. Our comprehensive review of existing datasets reveals the
limitations of current datasets: primarily, their lack of sufficient volume,
detailed clinical data and antepartum data. The OxMat dataset lays a foundation
for future AI-driven prenatal care, offering a robust resource for developing
and testing algorithms aimed at improving maternal and fetal health outcomes.