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TRV-2026-1024Version 1 · Certified

Written 2026-09-09 06:05:25 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-1024
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-09T06:05:25.556195Z
status: published
lens: trace
sector: health
headline: Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review
dek: 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 Januar…
gain_title: AI-assisted non-contact heart rate monitoring provides accurate, safe, and efficient neonatal assessment with strong correlation to ECG and rapid signal acquisition.
problem_title: Accuracy and clinical utility of AI-assisted non-contact neonatal heart rate monitoring remain unvalidated for routine implementation pending future multicenter studies.
trace_subject: AI-assisted non-contact heart rate monitoring for neonates compared to ECG
gain_reading: AI-assisted non-contact heart rate monitoring provides accurate, safe, and efficient neonatal assessment with strong correlation to ECG and rapid signal acquisition.
gain_evidence: These newer methods demonstrated a strong correlation with ECG readings, rapid signal acquisition, and improved robustness against motion and lighting variability | Emerging non-contact, AI-assisted HR monitoring technologies offer accurate, safe, and efficient alternatives for neonatal care, supporting faster clinical decisions and improved outcomes
problem_reading: Accuracy and clinical utility of AI-assisted non-contact neonatal heart rate monitoring remain unvalidated for routine implementation pending future multicenter studies.
problem_evidence: Future multicenter studies are required to validate accuracy and confirm clinical utility before routine clinical implementation
quick_read: A systematic review covering January 2013 to June 2025 examined non-contact and AI-assisted neonatal heart rate monitoring versus conventional ECG. It found a progressive shift toward camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems that showed strong correlation with ECG, rapid acquisition, and better robustness to motion and lighting.

For neonatal care, non-contact AI methods could reduce contact-related risks and enable faster viability assessment in the immediate postnatal period. As of the September 2026 publication date, the review positions these as promising alternatives but explicitly conditions routine use on future multicenter validation of accuracy and clinical utility.
limitation: 
tag: Dual reading
key_points: Systematic review searched PubMed, EMBASE, Google Scholar, and Cochrane from January 2013 through June 2025 following PRISMA guidelines. | Review documents shift from contact-based ECG and pulse oximetry to camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems. | Newer methods reported improved robustness against motion and lighting variability compared to conventional systems.
rundown: The review evaluated accuracy, reliability, and clinical applicability of emerging non-contact and AI-assisted heart rate monitoring technologies in neonates compared to conventional electrocardiography-based systems.

Authors searched four databases over a 12.5-year window and concluded these technologies support faster clinical decisions while noting validation gaps before routine adoption in immediate postnatal care.
sources:
- peer_reviewed | World Journal of Clinical Pediatrics | https://doi.org/10.5409/wjcp.119386 | 2026-09-09
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