NewbornTime – Improved newborn care based on video and artificial intelligence

Deprivation of oxygen to an infant during and after birth might lead to birth asphyxia, one of the leading causes of newborn deaths, cerebral palsy and other long-term damage. According to guidelines, a newborn in need of help to start breathing should be resuscitated immediately after birth. Resuscitation activities include stimulating, clearing airways, and perform bag-mask-ventilation. In Norway, approximately 10% of term infants need stimulation and around 3% need bag-mask ventilation.

NewbornTime will produce a timeline describing events and activities performed on a newborn. Accurate time of birth will be detected using AI models from thermal videos collected in the delivery room. Activity recognition will be performed using AI in the form of deep convolutional neural networks (CNN) on thermal and RGB video from the resuscitation. The system will be designed to recognize multiple time-overlapping activities. Care will be given to make the AI models robust, reliable, general, and adaptive to be able to use it at different hospitals and settings. The timelines will be used to evaluate compliance to guidelines and identify successful resuscitation activity patterns. It can further be useful in a de-briefing and quality improvement tool.

The project is a collaboration between University of Stavanger (UiS), Stavanger University Hospital (SUS), Laerdal Medical and BitYoga. UiS, SUS and Laerdal has long experience in collaborative research on newborn care. They have documented promising results on detecting activities using resuscitation videos from a hospital in Tanzania. In NewbornTime the data collection will be performed at SUS. BitYoga and Laerdal will ensure smart GDPR compliant data-contracts and data-platforms. UiS will develop site-adaptive AI methods for activity recognition in video.