Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization
Year: 2025
Type: article
Abstract: Around 5%–10% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from real newborn resuscitation emergency events. Generating and evaluating automated newborn resuscitation algorithm activity timelines relative to the Time of Birth (ToB) offers a promising opportunity to enhance newborn care practices. Given the importance of prompt resuscitation interventions within the ”golden minute” after birth, having an accurate ToB with second precision is essential for effective subsequent analysis of newborn resuscitation episodes. Instead, ToB is generally registered manually, often with minute precision, making the process inefficient and susceptible to error and imprecision. In this work, we explore the fusion of Artificial Intelligence (AI) and thermal imaging to develop the first AI-driven ToB detector. The use of temperature information offers a promising alternative to detect the newborn while respecting the privacy of healthcare providers and mothers. However, the frequent inconsistencies in measurements when using uncooled thermal sensors make normalization strategies critical. Our methodology involves a three-step process: first, we propose an adaptive normalization method based on Gaussian mixture models (GMM) to mitigate issues related to temperature variations; second, we implement an AI model to detect the presence of the newborn within the thermal video frames; and third, we evaluate and post-process the model’s predictions to estimate the ToB. A precision of 88.1% and a recall of 89.3% are reported in the detection of the newborn within thermal frames during performance evaluation. Our approach achieves an absolute median deviation of 2.7 s in estimating the ToB relative to the manual annotations. • Time of birth is pivotal for effective newborn resuscitation timeline analysis. • Current time of birth recording is manual, often imprecise with minute-level accuracy. • AI and thermal imaging offer a novel approach for precise time of birth detection. • Thermal cameras face challenges like inconsistent temperature readings and calibration issues. • Adaptive normalization using Gaussian mixture models mitigates temperature variations.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,080
Award: G8868001459
OpenAlex ID: W4403570975