AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case Study on Detecting Time of Birth

TL;DR

This paper introduces an AI-powered thermal video system that accurately detects the time of birth in healthcare settings, ensuring privacy and improving documentation for newborn resuscitation.

Contribution

It presents a novel privacy-preserving thermal imaging approach for automated ToB detection, achieving high precision and recall in clinical scenarios.

Findings

91.4% precision in ToB detection

97.4% recall in ToB detection

96% success rate with median deviation of 1 second

Abstract

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.