Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review
TL;DR
The analysis revealed a progressive shift from contact-based ECG and pulse oximetry to camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems that demonstrated a strong correlation with ECG readings, rapid signal acquisition, and improved robustness against motion and lighting variability.
Abstract
Background
Neonatal heart rate (HR) is an important parameter in the evaluation of newborn health and viability in the immediate postnatal period.
Aim
To evaluate the accuracy, reliability, and clinical applicability of emerging non-contact and artificial intelligence (AI)-assisted HR monitoring technologies in neonates compared to conventional electrocardiography (ECG)-based systems.
Methods
A comprehensive literature search was conducted across PubMed, EMBASE, Google Scholar, and Cochrane databases from January 2013 through June 2025 following PRISMA guidelines.
Results
The analysis revealed a progressive shift from contact-based ECG and pulse oximetry to camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems. These newer methods demonstrated a strong correlation with ECG readings, rapid signal acquisition, and improved robustness against motion and lighting variability.
Conclusion
Emerging non-contact, AI-assisted HR monitoring technologies offer accurate, safe, and efficient alternatives for neonatal care, supporting faster clinical decisions and improved outcomes. Future multicenter studies are required to validate accuracy and confirm clinical utility before routine clinical implementation.