Application of artificial intelligence gastroscope in blind area monitoring and independent image acquisition

Objective To analyze the blind area monitoring and independent image acquisition function of gastroscopic elves (a real-time gastroscopic monitoring system) in gastroscopy. Methods A total of 38 522 gastroscopic images from the database of Digestive Endoscopy Center of Renmin Hospital of Wuhan University were collected to train and validate the gastroscopic elves.Using computer to generate random numbers, 91 gastroscopic videos were selected to assess the position recognition accuracy of the gastroscopic elves, and 45 gastroscopic videos and matching gastroscopic images collected by endoscopists were selected to compare the coverage number and rate of gastroscopy sites between gastroscopic elves and endoscopists image acquisition. Two endoscopists entered the study to perform gastroscopies with or without gastroscopic elves. Forty-five gastroscopies respectively performed by the endoscopist A before and after usage of gastroscopic elves were collected, and 42 gastroscopies divided into 20 and 22 performed by the endoscopist B without use of gastroscopic elves in the same period were also collected. The coverage rate of gastroscopy sites was compared between the two endoscopists. Results The total position recognition accuracy of gastroscopic elves was 85.125% (1 156/1 358). The coverage rate of gastroscopic sites for the endoscopist A was (76.790±8.848)% and (87.325±7.065)%, respectively, before and after using gastroscopic elves, and the coverage rate in the same period for the endoscopist B was (75.926 ±11.565)% and (75.253 ±14.662)%, respectively. The coverage rate before using gastroscopic elves had no statistical difference between the two endoscopists (t=0.324, P=0.747). The coverage rate for the endoscopist A after using gastroscopic elves was higher than that before using gastroscopic elves (t=6.222, P=0.001), and that of the endoscopist B in the same period (t′=3.588, P=0.002). The coverage number and rate of gastroscopy sites for gastroscopic elves and endoscopists image acquisition were 20.956 ±3.406 and (77.613±12.613)%, and 15.467±2.296 and (57.284±8.503)%, respectively, with statistical differences (t=11.523, P=0.000; t=11.523, P=0.000). Conclusion Gastroscopic elves can improve the coverage number and rate of gastroscopy sites, and is worthy of promotion in clinics. Key words: Gastroscopy; Artificial intelligence; Blind area monitoring; Independent image acquisition