Real-Time Monitoring of Fetal Arrhythmias Using AI-Driven Cardiotocography Systems.

Abstract

Fetal arrhythmias represent a critical challenge in perinatal medicine, often leading to complications such as intrauterine growth restriction, heart failure, or stillbirth if not diagnosed and managed promptly. Traditional diagnostic techniques rely on intermittent ultrasound and “manual interpretation of cardiotocography (CTG)”, which are prone to observer variability and limited in their ability to detect subtle abnormalities in real time. “Advances in artificial intelligence (AI)-driven cardiotocography” systems provide new opportunities for continuous, automated, and objective monitoring of “fetal heart rate (FHR)” and uterine contractions. This study investigates the application of AI models, including deep learning and ensemble methods, to real-time CTG analysis for early detection of fetal arrhythmias. Experimental results show that AI-enhanced CTG systems achieve superior performance in arrhythmia classification compared to conventional approaches, with sensitivity exceeding 92% and specificity above 90%. Furthermore, real-time monitoring reduced false negatives, ensuring early interventions in high-risk pregnancies. Ethical concerns regarding data privacy, interpretability, and clinical integration are also discussed. The study concludes that AI-driven CTG systems represent a paradigm shift in perinatal care, offering scalable and accurate solutions for real-time fetal arrhythmia monitoring.