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IKTPLUSS-IKT og digital innovasjon

NewbornTime – Improved newborn care based on video and artificial intelligence

Alternative title: NewbornTime - Forbedret nyfødtomsorg basert på video og kunstig intelligens

Awarded: NOK 12.0 mill.

The NewbornTime project is about improved newborn care by using artificial intelligence (AI) for activity and event recognition in video from the time during and after birth. 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.

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.

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IKTPLUSS-IKT og digital innovasjon

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IKT forskningsområdeMenneske, samfunn og teknologiLTP2 Styrket konkurransekraft og innovasjonsevnePortefølje LivsvitenskapHelsePortefølje Muliggjørende teknologierSamfunnssikkerhetPolitikk- og forvaltningsområderForskningPortefølje HelseGrunnforskningPortefølje Industri og tjenestenæringerDigitalisering og bruk av IKTOffentlig sektorPolitikk- og forvaltningsområderHelse og omsorgFornyelse og innovasjon i offentlig sektorInnovasjonsprosjekter og prosjekter med forpliktende brukermedvirkningPolitikk- og forvaltningsområderLTP2 Innovasjon i stat og kommuneDigitalisering og bruk av IKTIKT forskningsområdeBransjer og næringerIKT-næringenLTP2 Helse, forebygging og behandlingLTP2 Utvikle fagmiljøer av fremragende kvalitetLTP2 Samfunnsikkerhet, sårbarhet og konfliktFornyelse og innovasjon i offentlig sektorLTP2 Samfunnssikkerhet og samhørighetInternasjonaliseringLTP2 Fornyelse i offentlig sektorInternasjonaliseringInternasjonalt prosjektsamarbeidAnvendt forskningLTP2 IKT og digital transformasjonLTP2 Muliggjørende og industrielle teknologierLTP2 Fagmiljøer og talenterPolitikk- og forvaltningsområderNæring og handelBransjer og næringerLTP2 Et kunnskapsintensivt næringsliv i hele landetPortefølje Demokrati, styring og fornyelsePortefølje Naturvitenskap og teknologiBransjer og næringerHelsenæringenPolitikk- og forvaltningsområderDigitaliseringIKT forskningsområdeKunstig intelligens, maskinlæring og dataanalyseIKT forskningsområdeVisualisering og brukergrensesnitt