Consent-driven, semi-automated data collection during birth and newborn resuscitation: Insights from the NewbornTime study

Abstract Accurate observations at birth and during newborn resuscitation are fundamental for quality improvement initiatives and research. However, manual data collection methods often lack consistency and objectivity, are not scalable, and may raise privacy concerns. The NewbornTime project aims to develop an AI system that generates accurate timelines from birth and newborn resuscitation events by automated video recording and processing, providing a source of objective and consistent data. This work aims to describe the implementation of the data collection system that is necessary to support the project’s purpose. Videos were recorded using thermal cameras in labor rooms and thermal and visual light cameras in resuscitation rooms. Consent from mothers were obtained before birth, and healthcare providers were given the option to delete videos by opting out. The video collection process was designed to minimize interference with ongoing treatment and not impose unnecessary burden on healthcare providers. Videos have been collected at Stavanger University Hospital since November 2021. By July 31st 2024, 645 thermal videos of birth and 186 visual light videos of resuscitation have been collected. Data collection and development and implementation of AI systems is still ongoing. The utilization of automated data collection and AI video processing around birth may allow for consistent and enhanced documentation, quality improvement initiatives, and research opportunities on sequence, timing and duration of treatment activities during acute events, with less efforts needed for capturing data and improved privacy for participants. Author summary In our study, we address the need for accurate and consistent data collection during birth and newborn resuscitation. Traditional manual data collection methods often fall short in terms of objectivity, consistency, scalability, and privacy. To overcome these challenges, we have developed a consent-driven, semi-automated data collection system with the aim of developing AI-based systems that uses video recordings to create precise timelines of events occurring at birth and during newborn resuscitation. This method leverages thermal and visual light cameras to capture footage, ensuring minimal interference with clinical practices and respecting the privacy of participants. This work describes the detailed setup of the data collection and sets it in perspective to existing solutions used in clinical practice and research. Since November 2021, we have collected data at Stavanger University Hospital, including 645 thermal videos of births and 186 visual light videos of resuscitation by the end of July 2024. This data will support the future development of AI algorithms aimed at enhancing the objectivity and consistency of data collection. Ultimately objective data can be used for individual learning or, on a big scale, understanding adherence to guidelines and effect of treatment, when combined with outcome data. Competing Interest Statement S.B. and A.J. are a full-time employees of Laerdal Medical AS. Ø. M.-B. and K.E. (starting June 2024, 20%) are part time employees at Laerdal Medical AS. Ö.C. is a part time employee of BitUnitor AS. The remaining authors declare no competing interests. Funding Statement The project is funded by Norwegian Research council (NRC), project number 320968. Additional funding has been provided by Helse Vest Innovation grant, Fondation Idella and Helse Campus, UiS. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Regional Ethical Committee, region west, Norway (REK-Vest) gave ethical approval for this work. REK number: 222455. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Data used in this research includes videos from labor and newborn resuscitation. Due to the potentially identifiable nature of the dataset the authors are prevented from publicly sharing video data under the ethical approvals for this study. For more information, contact: kjersti.engan{at}uis.no