Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms

Fetal health monitoring through one-dimensional Doppler ultrasound (DUS) signals offers a cost-effective and accessible approach that is increasingly gaining interest. Despite its potential, the development of machine learning based techniques to assess the health condition of mothers and fetuses using DUS signals remains limited. This scarcity is primarily due to the lack of extensive DUS datasets with a reliable reference for interpretation and data imbalance across different gestational ages. In response, we introduce a novel autoregressive generative model designed to map fetal electrocardiogram (FECG) signals to corresponding DUS waveforms (Auto-FEDUS). By leveraging a neural temporal network based on dilated causal convolutions that operate directly on the waveform level, the model effectively captures both short and long-range dependencies within the signals, preserving the integrity of generated data. Cross-subject experiments demonstrate that Auto-FEDUS outperforms conventional generative architectures across both time and frequency domain evaluations, producing DUS signals that closely resemble the morphology of their real counterparts. The realism of these synthesized signals was further gauged using a quality assessment model, which classified all as good quality, and a heart rate estimation model, which produced comparable results for generated and real data, with a Bland-Altman limit of 4.5 beats per minute. This advancement offers a promising solution for mitigating limited data availability and enhancing the training of DUS-based fetal models, making them more effective and generalizable.

Edge AI for Real-time Fetal Assessment in Rural Guatemala

Perinatal complications, defined as conditions that arise during pregnancy, childbirth, and the immediate postpartum period, represent a significant burden on maternal and neonatal health worldwide. Factors contributing to these disparities include limited access to quality healthcare, socioeconomic inequalities, and variations in healthcare infrastructure. Addressing these issues is crucial for improving health outcomes for mothers and newborns, particularly in underserved communities. To mitigate these challenges, we have developed an AI-enabled smartphone application designed to provide decision support at the point-of-care. This tool aims to enhance health monitoring during pregnancy by leveraging machine learning (ML) techniques. The intended use of this application is to assist midwives during routine home visits by offering real-time analysis and providing feedback based on collected data. The application integrates TensorFlow Lite (TFLite) and other Python-based algorithms within a Kotlin framework to process data in real-time. It is designed for use in low-resource settings, where traditional healthcare infrastructure may be lacking. The intended patient population includes pregnant women and new mothers in underserved areas and the developed system was piloted in rural Guatemala. This ML-based solution addresses the critical need for accessible and quality perinatal care by empowering healthcare providers with decision support tools to improve maternal and neonatal health outcomes.