Ubenwa, an innovative healthtech startup that leverages artificial intelligence and machine learning to diagnose infants within 0–6 months of age, has locked in $2.5 million in pre-seed funding to scale its operations and materialise its go-to-market strategy.
The Montreal-based startup was founded in 2017 by Charles Onu, a Nigerian whose experience in the medical field and extensive AI practice combined to birth the vision of Ubenwa.
Ubenwa is pioneering an automated sound-based diagnostic solution for infants by leveraging cutting-edge AI and several years of scientific research.
Ubenwa, though an independent startup, is a spin-off of research Onu’s team has conducted since 2017 in Mila, a world-renowned AI hub in Quebec, Canada. However, Canada is not the only country where Ubenwa’s impact is being felt. Through strategic partnerships with leading hospitals in Nigeria, Brazil, and Canada, Ubenwa boasts of having the largest and most diverse database of infant cry sounds that have been clinically annotated.
In a statement shared with TechCabal, Onu, who is also Ubenwa’s CEO, described Ubenwa’s solution as one that helps distinguish an infant’s natural cries from the disease-causing cries for help.
“Ubenwa is building a diagnostic tool that understands when a baby’s cry is actually a cry for medical attention,” he said. “Ultimately, our goal is to be a translator for baby cry sounds, providing a non-invasive way to monitor medical conditions everywhere you find a baby: delivery rooms, neonatal and paediatric intensive care units, nurseries, and even homes.”
Per the press release, Ubenwa has developed algorithms to track cry activity, detect acoustic biomarkers, and predict anomalies, thereby converting infant cries to potential diagnoses. In a successful pilot to detect neurological injury due to birth asphyxia, Ubenwa’s software showed a 40% improvement over APGAR scoring, the widely canonised physical examination at birth.