Birth Asphyxia is one of the top 3 causes of infant mortality in the world, causing the death of about 1.2 million infants and severe life-long disabilities (such as cerebral palsy, deafness, and paralysis) to an equal number annually. "If newborns who have asphyxia can be detected early enough, we may be able to save their lives" - UN.
We are developing a machine learning system that can take as input the infant cry, analyse the amplitude and frequency patterns in the cry, to provide instant diagnosis of birth asphyxia. The test results from our diagnostic software have shown a Sensitivity of over 86% and Specificity of 89%.
For testing, our algorithm has been deployed as a mobile app which harnesses the processing capabilities of smartphones to provide near-instantaneous assessment of whether or not a newborn has or is at risk of asphyxia. Ubenwa is non-invasive and can be over 95% cheaper than existing clinical alternative.
Ubenwa was recognized by the World Health Organisation as one of top 30 healthcare innovators in Africa.
A blog post reflecting on the team's challenges and accomplishments of 2018, and providing a window into what is to come in 2019.
Ubenwa is one of 30 teams that have qualified for the 3rd round of AI XPrize competition. [Update: The Ubenwa team voluntarily exited the AI XPrize as of June 6, 2019]
Charles gave a talk on the show - This Week in Machine Learning and AI (TWiML). Listen here.
Unveiling of the Ubenwa mobile app at the Workshop on Machine Learning for the Developing World at NIPS 2017.
Charles presented Ubenwa at the Montreal AI Symposium. Watch here and Innocent pitched Ubenwa at ITU Telecom World in Busan, South Korea.
We participated as exhibitors at the first United Nations AI for Good Global Summit in Geneva.
Ubenwa has been selected as one of 141 teams from around the world to compete in the IBM AI XPrize . The AI XPrize is a $5million contest aiming to reward teams applying AI to address grand challenges.
Ubenwa has been accepted into the startup mentorship program at District 3. District 3 is a leading startup accelerator based in Montreal, Canada.
We presented early work on Ubenwa, receiving the best paper at Machine Learning for Healthcare (MLHC) workshop at Neural Information Processing Systems (NIPS) 2015 conference.
Jon has a background in the arts and computer science. He co-founded and co-ran Paradem Consulting for 5 years building it into a successful software and cloud services consulting company.
Clinical Development Lead
Samantha is a PhD Candidate in Experimental Medicine at McGill University.
Professor of Biomedical Eng
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