Edge AI for Real-time Fetal Assessment in Rural Guatemala

2025-03-12

AI Summary

In resource-constrained settings—such as rural Guatemala—high perinatal complication rates and lack of real-time clinical assessment capabilities pose critical challenges. To address this, we propose a lightweight edge AI system that, for the first time, deploys an integrated fetal heart rate and uterine contraction pattern analysis model directly on Android smartphones, enabling fully offline, low-latency (<800 ms) identification of fetal distress risk. The system leverages TensorFlow Lite and optimized signal processing algorithms, implemented in Kotlin for cross-platform compatibility and seamless integration into community midwife home-visit workflows. Field deployment demonstrated 92% accuracy in fetal distress detection under zero-connectivity conditions, supporting over 300 antenatal assessments. Key contributions include: (1) an edge-native AI architecture explicitly designed for resource-limited environments; (2) clinical-grade, cloud-independent real-time inference; and (3) a scalable, lightweight paradigm for maternal and newborn health interventions adaptable to other low-income regions.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Develop AI tool for real-time fetal assessment in rural areas.
• Address perinatal complications in underserved communities with limited healthcare.
• Empower midwives with ML-based decision support during home visits.

Innovation

• AI-enabled smartphone app for real-time fetal assessment
• Integrates TensorFlow Lite and Python-based algorithms
• Designed for low-resource settings like rural Guatemala