深度序列学习用于通过$\$$ 25多普勒设备准确估计胎龄
Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device
摘要：
评估胎儿发育通常是通过超声成像等技术进行的，由于有效操作这些设备所需的成本、维护、技能和培训很高，农村地区通常无法获得这种技术。在这项工作中，我们提出了一个低成本的一维多普勒为基础的方法估计胎龄（GA）。使用智能手机从401例5至9个月GA的妊娠中收集多普勒时间序列。所提出的GA估计模型是基于序列学习，通过使用卷积长短期记忆网络形成时间依赖模型。从多普勒信号中提取时频特征，并在输入网络之前进行正则化。总的平均绝对GA误差相对于最后一次月经期被发现是0.71个月，这优于所有以前的作品。
英文摘要：
Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estimating gestational age (GA). Doppler time series were collected from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed model for GA estimation is based on sequence learning by forming a temporally dependent model using a convolutional long-short-term memory network. Time-frequency features are extracted from Doppler signals and regularized before feeding to the network. The overall mean absolute GA error with respect to the last menstrual period was found to be 0.71 month, which outperforms all previous works.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
