Ubenwa: Cry-based Diagnosis of Birth Asphyxia

TL;DR

Ubenwa is a machine learning system that analyzes infant cries via smartphones to enable early, cost-effective diagnosis of birth asphyxia, especially in resource-limited settings.

Contribution

The paper introduces Ubenwa, a novel cry-based diagnostic tool using machine learning for neonatal asphyxia detection, suitable for deployment on smartphones and wearables.

Findings

Potential for rapid, low-cost diagnosis in resource-poor settings

Automated cry analysis achieves high accuracy

Reduces need for specialized medical expertise

Abstract

Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early detection of asphyxia in newborns is very difficult in many parts of the world, especially in resource-poor settings. We are developing a machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia through automated analysis of the infant cry. Deployed via smartphone and wearable technology, Ubenwa will drastically reduce the time, cost and skill required to make accurate and potentially life-saving diagnoses.