AI Center, Korea University College of Medicine, Seoul, Korea.
Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
J Med Syst. 2023 Aug 3;47(1):82. doi: 10.1007/s10916-023-01960-1.
This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with the web server for the woman and the computer server for the expert). Data came from 5249 (or 4833) cardiotocography traces in Anam Hospital for the mobile application (or the computer server). 150 data cases of 5-minute duration were extracted from each trace with 141,001 final cases for the mobile application and for the computer server alike. The dependent variable was fetal status with two categories (Normal, Abnormal) for the mobile application and three categories (Normal, Middle, Abnormal) for the computer server. The fetal heart rate served as a predictor for the mobile application and the computer server, while uterus contraction for the computer server only. The 1-dimension (or 2-dimension) Resnet CNN was trained for the mobile application (or the computer server) during 800 epochs. The sensitivity, specificity and their harmonic mean of the 1-dimension CNN for the mobile application were 94.9%, 91.2% and 93.0%, respectively. The corresponding statistics of the 2-dimension CNN for the computer server were 98.0%, 99.5% and 98.7%. The average inference time per 1000 images was 6.51 micro-seconds. Deep learning provides an efficient model for the real-time classification of fetal status in the mobile application and the computer server at the same time.
这项研究使用卷积神经网络 (CNN) 和胎儿监护图数据，实时分类孕妇移动应用程序和数据专家计算机服务器中的胎儿状态（传感器与智能手机相连，智能手机与妇女的网络服务器和专家的计算机服务器相连）。数据来自 Anam 医院的 5249 条（或 4833 条）胎儿监护图轨迹，用于移动应用程序（或计算机服务器），从每条轨迹中提取 150 个 5 分钟时长的数据案例，最终得到 141001 个案例，移动应用程序和计算机服务器的数据案例相同。因变量为胎儿状态，移动应用程序有两个类别（正常、异常），计算机服务器有三个类别（正常、中等、异常）。胎儿心率为移动应用程序和计算机服务器提供预测因子，而子宫收缩仅为计算机服务器提供预测因子。一维（或二维）Resnet CNN 为移动应用程序（或计算机服务器）进行了 800 个时期的训练。一维 CNN 移动应用程序的敏感性、特异性和调和平均值分别为 94.9%、91.2%和 93.0%。计算机服务器二维 CNN 的对应统计数据分别为 98.0%、99.5%和 98.7%。每 1000 张图像的平均推断时间为 6.51 微秒。深度学习为移动应用程序和计算机服务器的胎儿状态实时分类提供了一种高效的模型。
