Emerging technologies like deep learning algorithms and wearable sensors may improve neonatal SVT detection, though neonatal-specific validation is crucial.
Emerging technologies such as AI and wearable sensors show promise for improving neonatal SVT detection, but require rigorous validation in this specific population.
Absolute Event Rate: 0% vs 0%
Neonatal supraventricular tachycardia (SVT) represents the most common pathological tachyarrhythmia in the neonatal period, with an incidence of 1:250–1000 live births. This review synthesizes current diagnostic methodologies and explores the potential of emerging technological innovations. Traditional modalities, including 12-lead electrocardiography (ECG) and ambulatory monitoring, remain foundational but face limitations regarding signal quality and intermittent capture in neonates. Emerging technologies—specifically deep learning algorithms, biocompatible wearable sensors, and non-contact sensing modalities—offer promising avenues to enhance detection. While AI models in broader pediatric cohorts have reported diagnostic accuracies exceeding 90%, neonatal-specific validation remains a critical need. This review discusses the integration of these tools into clinical workflows, highlighting potential improvements in diagnostic timing while addressing persistent technical, regulatory, and ethical barriers. We provide a framework for clinicians navigating this evolving landscape, emphasizing the need for rigorous validation of new technologies in the unique neonatal population.
Qi et al. (Mon,) reported a other. Emerging technologies like deep learning algorithms and wearable sensors may improve neonatal SVT detection, though neonatal-specific validation is crucial.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: