Objective:To evaluate the effect of automated flexible endoscope channel brushing system (AFECBS) on endoscope reprocessing.Methods:A prospective randomized controlled study was conducted. The used endoscopes were divided into automatic group and manual group by random number table method, 200 in each group. In the automatic group, the AFECBS was used to scrub each tube 3 times during endoscope cleaning; and in the manual group, scrubbing and disinfection personnel routinely brushed each pipeline for 3 times. The primary end point was the qualified rate of endoscopic cleaning quality in the two groups, and the secondary end point was the time spent by the scrubbing and disinfection personnel on the two groups.Results:The qualified rate of overall cleaning in the automatic group was 90.0% (180/200), and in the manual group was 81.0% (162/200). The qualified rate of the automatic group was higher than that of the manual group ( χ2=6.534, P=0.011). The qualified rate of gastroscope cleaning in the automatic group was higher than that in the manual group [92.0% (127/138) VS 81.6% (120/147), χ2=6.658, P=0.010]. There was no significant difference in the qualified rate of colonoscope cleaning between the automatic group and the manual group [85.5% (53/62) VS 79.2% (42/53), χ2=0.774, P=0.379]. When the cleaning personnel scoured 5 endoscopes in each of the two groups, the time of the automatic group (5.17±0.42 min) was shorter than that of the manual group (9.60±0.53 min) ( t=92.644, P<0.001). Conclusion:Compared with manual scrubbing, AFECBS can improve the qualified rate of endoscope cleaning and the work efficiency of scrubbing and disinfection personnel, which is worthy of clinical application.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.