# Ubenwa: Cry-based Diagnosis of Birth Asphyxia

arXiv: https://arxiv.org/abs/1711.06405 · HTML: https://mlsanity.com/paper/1711.06405/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Self-supervised learning for infant cry analysis](https://mlsanity.com/paper/2305.01578/) (2023)
- [Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review](https://mlsanity.com/paper/2304.07542/) (2023)
- [CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds](https://mlsanity.com/paper/2305.00969/) (2023)
- [HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning](https://mlsanity.com/paper/2412.01167/) (2024)

## Abstract (arXiv)

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.