Engan et al. BMC Digital Health (2023) 1:10 https://doi.org/10.1186/s44247-023-00010-7 STUDY PROTOCOL © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Open Access BMC Digital Health Newborn Time - improved newborn care based on video and artifcial intelligence - study protocol Kjersti Engan 1* , Øyvind Meinich-Bache 1,2 , Sara Brunner 2 , Helge Myklebust 2 , Chunming Rong 1,3 , Jorge García-Torres 1 , Hege L. Ersdal 4,5 , Anders Johannessen 2 , Hanne Markhus Pike 6 and Siren Rettedal 4,6 Abstract Background Approximately 3-8% of all newborns do not breathe spontaneously at birth, and require time critical resuscitation. Resuscitation guidelines are mostly based on best practice, and more research on newborn resucitation is highly sought for. Methods The NewbornTime project will develop artificial intelligence (AI) based solutions for activity recognition during newborn resuscitations based on both visible light spectrum videos and infrared spectrum (thermal) videos. In addition, time-of-birth detection will be developed using thermal videos from the delivery rooms. Deep Neural Network models will be developed, focusing on methods for limited supervision and solutions adapting to on-site environments. A timeline description of the video analysis output enables objective analysis of resuscitation events. The project further aims to use machine learning to find patterns in large amount of such timeline data to better understand how newborn resuscitation treatment is given and how it can be improved. The automatic video analysis and timeline generation will be developed for on-site usage, allowing for data-driven simulation and clinical debrief for health-care providers, and paving the way for automated real-time feedback. This brings added value to the medi- cal staff, mothers and newborns, and society at large. Discussion The project is a interdisciplinary collaboration, combining AI, image processing, blockchain and cloud technology, with medical expertise, which will lead to increased competences and capacities in these various fields. Trial registration ISRCTNregistry, number ISRCTN12236970 Keywords Newborn resuscitation, Artificial intelligence, Video, Thermal video, Timeline *Correspondence: Kjersti Engan kjersti.engan@uis.no 1 Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway 2 Laerdal Medical AS, Stavanger, Norway 3 BitYoga, Stavanger, Norway 4 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway 5 Department of Anaesthesia, Stavanger University Hospital, Stavanger, Norway 6 Department of Pediatrics, Stavanger University Hospital, Stavanger, Norway