Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania.
Second Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania.
Sensors (Basel). 2021 Dec 30;22(1):253. doi: 10.3390/s22010253.
High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest-the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.
高分辨率食管测压用于研究食管动力障碍，使用带有多达 36 个传感器的导管。生成和分析彩色压力地形图，并使用芝加哥算法建立最终诊断。该算法的主要参数之一是整合松弛压力（IRP）。该过程耗时。我们的目的是首先开发一种基于机器学习的解决方案来检测探头定位故障，并创建一个分类器，仅根据原始图像自动确定 IRP 是否在正常范围内或高于截止值。第一步是对图像进行预处理，找到感兴趣的区域——吞咽的确切时刻。然后，对图像进行调整大小和缩放，以便将其用作深度学习模型的输入。我们使用 InceptionV3 深度学习模型对图像进行分类，以确定导管定位是否正确或失败，并确定 IRP 的准确类别。对于这两个问题，训练好的卷积神经网络的准确率都在 90%以上。这项工作只是完全自动化芝加哥分类的第一步，可以减少人为干预。
