Consent-driven, semi-automated data collection during birth and newborn resuscitation: Insights from the NewbornTime study
Authors: Brunner Sara , Johannessen Anders , García-Torres Jorge , Catak Ferhat Özgur , Meinich-Bache Øyvind , Rettedal Siren , Engan Kjersti 
Published: January 2025

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 31st2024, 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.