Deep Learning Assisted Mouth-Esophagus Passage Time Estimation During Gastroscopy

Abstract

A gastroscopy involves examining the upper digestive system using a flexible tube equipped with a small camera. Generally, it is performed to determine the cause of digestive symptoms, such as vomiting blood, stomach pains, and difficulty swallowing. Though this procedure has been performed since the mid-19th century, and various measures have been implemented to make it easier and less invasive, it is still not risk-free. One of the major complications is esophagus perforation, and most of them happen during the insertion of the gastroscopy. Therefore, it is necessary to develop an effective method for evaluating the performance of the operator. One appropriate metric is the time interval between the mouth and esophagus during the intubation. In this paper, we propose a gastroscopy video processing system based on deep learning to automatically evaluate the mouth-esophagus passage time. In this system, a Convolutional Neural Network (CNN) based model is adopted to detect the mouth and esophagus, track the timestamps of the last appearance of the mouth and the first appearance of the esophagus, and calculate the interval between those appearances. Our system is capable of dealing with abnormal circumstances that can occur during a procedure, as well as reporting accurate results. Experiment results show that our best model achieves an accuracy of 88.92% on image dataset, and an accuracy of 99.86% on videos for the mouth-esophagus passage time.