Arain Zara, Iliodromiti Stamatina, Slabaugh Gregory, David Anna L, Chowdhury Tina T
Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
Curr Res Physiol. 2023 May 19;6:100099. doi: 10.1016/j.crphys.2023.100099. eCollection 2023.
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
机器学习技术以及人工智能工具的转化应用以提升患者体验，正在改变产科和孕产妇护理。越来越多的预测工具已利用来自电子健康记录、诊断成像和数字设备的数据得以开发。在本综述中，我们探讨了机器学习的最新工具、建立预测模型的算法以及评估胎儿健康状况、预测和诊断诸如妊娠期糖尿病、先兆子痫、早产和胎儿生长受限等产科疾病所面临的挑战。我们讨论了机器学习方法和智能工具在胎儿异常自动诊断成像以及利用超声和磁共振成像评估胎儿胎盘和宫颈功能方面的快速发展。在产前诊断中，我们讨论了用于胎儿、胎盘和宫颈磁共振成像序列分析以降低早产风险的智能工具。最后，将讨论利用机器学习提高产时护理安全标准和早期发现并发症的问题。对增强产科和孕产妇诊断与治疗技术的需求应改进患者安全框架并提升临床实践水平。
