A Nigerian startup has developed a machine learning system to detect child birth asphyxia earlier and hopes to save thousands of babies’ lives every year when its technology is deployed.
The founders say the AI solution has achieved over 95% prediction accuracy in trials with nearly 1,400 pre-recorded baby cries. The startup is now raising funds to acquire more data to improve accuracy and obtain clinical approval from health institutions.
The startup is already garnering international attention and is in the final round for the global IBM Watson AI XPRIZE competition, which has a $5 million prize.
Birth asphyxia is the third highest cause of under-five child deaths and is responsible for almost one million neonatal deaths annually, according to WHO. It has also been linked to 1.1 million intrapartum stillbirths, long-term neurological disability and impairment.
Charles Onu, Ubenwa’s founder and principal innovator, explained that the startup’s machine learning system takes an infant’s cry as input, analyses the amplitude and frequency patterns of the cry and provides instant diagnosis of birth asphyxia.
Although the condition is detectable, Onu said few public hospital in the country had the equipment due to its high cost, poor electricity service and an unrealistic routine application for every child.
Ubenwa’s co-founder and engineering lead, Udeogu Innocent, said after being able to achieve a level of success with the model, the startup then deployed its technology to a mobile app for easier mobile diagnosis of birth asphyxia. It builds on techniques that have been developed for speech recognition.
The Ubenwa team is conducting clinical validation exercises in Nigeria at the University of Port Harcourt Teaching Hospital and in Canada at the McGill University Health Centre.
