Baker Stephanie, Yogavijayan Thiviya, Kandasamy Yogavijayan
College of Science and Engineering, James Cook University, Cairns, QLD 4878, Australia.
College of Medicine and Dentistry, James Cook University, Townsville, QLD 4811, Australia.
Healthcare (Basel). 2023 Dec 6;11(24):3107. doi: 10.3390/healthcare11243107.
Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.
早产是指在妊娠满37周之前的活产。全球每年约有11%的婴儿早产。血压（BP）监测对于维持早产儿的血流动力学稳定至关重要，并会影响预后。然而，目前的方法存在许多相关局限性，包括有创测量、不准确以及感染风险。在这篇叙述性综述中，我们发现基于从非侵入性传感器获得的数据，人工智能（AI）是在新生儿队列中持续测量血压的一种有前景的工具。我们的研究结果突出了新生儿队列中血压传感的关键传感技术、AI技术和模型评估指标。此外，我们的研究结果表明，利用AI的非侵入性血压监测在成人队列中已显示出前景，但尚未在新生儿队列中得到广泛探索。我们得出结论，在开发一种创新方法以提供现有连续血压监测方法的非侵入性替代方案方面存在重大研究机会，这有可能改善早产儿的预后。
