ObjectiveTo construct an intelligent performance measurement system of gastrointestinal endoscopy and to analyze its value for endoscopic quality improvement. MethodsThe intelligent gastrointestinal endoscopy performance measurement system was developed by using the deep convolutional neural network (DCNN) and deep reinforcement learning, based on the Digital Imaging and Communications in Medicine. Images were acquired of patients undergoing gastrointestinal endoscopy at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from December 2016 to October 2018. The system applied cecum recognition model (DCNN1), images in vitro and in vivo recognition model (DCNN2), and identification model at 26 gastric sites (DCNN3) to monitor indices such as cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage. Images of 83 gastroscopies and 205 colonoscopies acquired at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March to November 2019 were randomly selected to examine the effectiveness of the system. ResultsThe intelligent gastrointestinal endoscopy performance measurement system consisted of quality analysis of both gastroscopy and colonoscopy, including all indices, and could be generated automatically at any time. The accuracy for cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage were 92.5% (172/186), 91.7% (188/205), 100.0% (83/83), 89.3% (1 928/2 158), respectively. ConclusionThe intelligent performance measurement system for gastrointestinal endoscopy can be recommended for the quality control of gastrointestinal endoscopy, from which endoscopists can get feedback and improve the quality of gastrointestinal endoscopy.