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

arXiv: https://arxiv.org/abs/2502.04365 · HTML: https://mlsanity.com/paper/2502.04365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [Activity Recognition From Newborn Resuscitation Videos](https://mlsanity.com/paper/2303.07789/) (2023)
- [Advancing Newborn Care: Precise Birth Time Detection Using AI-Driven Thermal Imaging with Adaptive Normalization](https://mlsanity.com/paper/2410.10483/) (2024)

## Built on by (most cited first, 1 of 1)

- [Two-Stream Thermal Imaging Fusion for Enhanced Time of Birth Detection in Neonatal Care](https://mlsanity.com/paper/2503.03244/) (2025)

## Abstract (arXiv)

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.