4 papers · 1 filter
PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL
Sheng Wong, Ravi Shankar, Beth Albert +5
Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes…
Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder
John Tolladay, Beth Albert, Gabriel Davis Jones
Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpre…
Large language models surpass domain-specific architectures for antepartum electronic fetal monitoring analysis
Sheng Wong, Ravi Shankar, Beth Albert +1
Foundation models (FMs) and large language models (LLMs) have demonstrated promising generalization across diverse domains for time-series analysis, yet their potential for electro…
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 +3
The rapid advancement of Artificial Intelligence (AI) in healthcare presents a unique opportunity for advancements in obstetric care, particularly through the analysis of cardiotoc…