CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera.
CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera.
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DOI：
10.3389/frai.2021.543176
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发表时间：
2021
影响因子：
4
通讯作者：
Clifford GD
中科院分区：
文献类型：
--
作者：
Kulkarni SS;Katebi N;Valderrama CE;Rohloff P;Clifford GD
妊娠期常规血压（BP）测量通常使用自动血压计设备进行。由于在先兆子痫人群中还没有验证无线血压测量设备，因此需要一种简单的方法来捕获来自此类设备的读数，特别是在低资源环境中，其中将BP数据从现场传输到中心位置是分流的重要机制。为此，使用手机摄像头从标准Omron M7自充气BP袖带的液晶显示器（LCD）屏幕上采集了总计8192个BP读数。一组49名非专业助产士在危地马拉农村进行常规筛查时，从1697名孕龄在6周至40周之间的单胎孕妇中获得了这些数据。由于方向和视差的变化;环境因素如照明、阴影;以及图像采集因素如运动模糊和焦点问题，图像在外观上表现出广泛的变化。由三名注释者（BP范围：34-203 mm Hg）独立标记图像的可读性和质量，并解决不一致。提出了一种基于轮廓的LCD图像预处理方法，并将其自动分割为舒张压、收缩压和心率。然后训练深度卷积神经网络，使用多位识别方法将LCD图像转换为数值。在可读的低质量和高质量图像上，该方法实现了91%的分类准确率和收缩压的平均绝对误差为3.19 mm Hg，舒张压的准确率为91%，平均绝对误差为0.94 mm Hg。当排除低质量图像时，这些误差值在FDA BP监测指南范围内。所提出的方法的性能被证明是大大优于最先进的开源工具（Tesseract和Google Vision API）的上级。该算法的开发使得它可以部署在手机上，并且在没有网络连接的情况下工作。
Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34–203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.