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NewbornTime – Improved newborn care based on video and artificial intelligence


Description:

Birth asphyxia is a primary cause of death in newborns as well as the main cause of cerebral palsy and other development disorders in children, and immediate resuscitation of the newborn is crucial to reduce the risk. The NewbornTime project will provide a tool for quality improvement of newborn resuscitation both at a macro level, challenging current guidelines, and at a micro level providing a debriefing and quality improvement tool. Ultimately, this can have a significant impact in reducing long-term damage and save lives. The project aims to utilize video recordings from births and newborn resuscitation situations to develop an Artificial Intelligence (AI) based system, NewbornTimeline, for automatic timeline generation of time of birth as well as potential resuscitation activities like ventilation, stimulation, suction, and the number of health care providers involved. The system input will be based on thermal video from the delivery room and RGB (+ thermal) video from the resuscitation table. NewbornTime will use thermal cameras in the delivery room and develop video processing algorithms to accurately detect the time of birth. Potential obstacles between newborns and cameras require multiple thermal cameras and real-world data to develop robust algorithms. The project aims to explore semi-supervised learning of Deep Neural Networks (DNNs) for partly un-labeled and untrimmed videos for activity recognition during newborn resuscitation, as labeling of activities will be both time consuming and have privacy issues. There will also be a focus on solutions that can adapt to on-site environments, since variations between hospitals in different countries can be extensive. NewbornTime will develop a GDPR compliant and secure digital patient consent handling system and cloud-based storage for sensitive data. Such solutions can be transferred to other areas of video activity recognition on sensitive data for medical situations, as well as other non-sensitive data.


Project leader: Kjersti Engan

Started: 2021

Ends: 2024

Category: Universiteter

Sector: UoH-sektor

Budget: 11955000

Institution: Institutt for data- og elektroteknologi

Address: Stavanger