AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case Study on Detecting Time of Birth
This work addresses the need for precise ToB documentation to improve newborn resuscitation outcomes in healthcare, representing an incremental advancement by applying existing AI methods to a new privacy-preserving thermal imaging context.
The study tackled the problem of inaccurate manual recording of Time of Birth (ToB) in newborns by developing an AI-driven thermal video analysis system, achieving 91.4% precision and 97.4% recall in detection with a median deviation of 1 second from manual annotations.
Approximately 10% of newborns need some assistance to start breathing and 5\% proper ventilation. It is crucial that interventions are initiated as soon as possible after birth. Accurate documentation of Time of Birth (ToB) is thereby essential for documenting and improving newborn resuscitation performance. However, current clinical practices rely on manual recording of ToB, typically with minute precision. In this study, we present an AI-driven, video-based system for automated ToB detection using thermal imaging, designed to preserve the privacy of healthcare providers and mothers by avoiding the use of identifiable visual data. Our approach achieves 91.4% precision and 97.4% recall in detecting ToB within thermal video clips during performance evaluation. Additionally, our system successfully identifies ToB in 96% of test cases with an absolute median deviation of 1 second compared to manual annotations. This method offers a reliable solution for improving ToB documentation and enhancing newborn resuscitation outcomes.