Alkhodari Mohanad, Widatalla Namareq, Wahbah Maisam, Al Sakaji Raghad, Funamoto Kiyoe, Krishnan Anita, Kimura Yoshitaka, Khandoker Ahsan H
Department of Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates.
Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.
Front Cardiovasc Med. 2022 Jul 29;9:926965. doi: 10.3389/fcvm.2022.926965. eCollection 2022.
In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20-40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices.
在过去二十年中，死产在全球范围内导致了约200万例胎儿死亡。尽管当前的超声工具可可靠地用于评估孕期胎儿的生长情况，但它仍存在对胎儿的安全问题，需要专业人员操作，并且在欠发达国家还存在经济方面的顾虑。在此，我们提出了深度相干性，这是一种新颖的人工智能（AI）方法，它依靠1分钟的无创心电图（ECG）来阐释孕期母体与胎儿心跳之间的关联。我们使用一个经过训练的深度学习工具，对从172名孕妇（20至40周）收集的总共941个1分钟的母胎R波峰段进行分析，验证了该方法的性能。该工具在识别耦合情况时达到的高准确率（90%）证明了使用人工智能作为频繁评估胎儿发育的监测工具的潜力。深度学习在解释母体与胎儿心跳之间的同步机制方面具有重要意义。这项研究可能为在临床实践中整合自动化深度学习工具铺平道路，以便在减少与当前临床设备相关的分诊、副作用和成本的同时，提供及时且持续的胎儿监测。
