Ubenwa: Cry-based Diagnosis of Birth Asphyxia
This addresses the critical issue of early detection of birth asphyxia, especially in resource-poor settings where current diagnostic methods are too sophisticated and inaccessible.
The researchers tackled the problem of diagnosing birth asphyxia in newborns, which is a leading cause of neonatal mortality, by developing Ubenwa, a machine learning system that analyzes infant cries via smartphone and wearable technology, aiming to drastically reduce the time, cost, and skill required for accurate diagnosis.
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.