BACKGROUND AND AIMS: Quality control can decrease variations in the performance of colonoscopists and improve the effectiveness of colonoscopy to prevent colorectal cancers. Unfortunately, routine quality control is difficult to carry out because a practical method is lacking. The aim of this study was to develop an automatic quality control system (AQCS) and assess whether it could improve polyp and adenoma detection in clinical practice. METHODS: First, we developed AQCS based on deep convolutional neural network models for timing of the withdrawal phase, supervising withdrawal stability, evaluating bowel preparation, and detecting colorectal polyps. Next, consecutive patients were prospectively randomized to undergo routine colonoscopies with or without the assistance of AQCS. The primary outcome of the study was the adenoma detection rate (ADR) in the AQCS and control groups. RESULTS: A total of 659 patients were enrolled and randomized. A total of 308 and 315 patients were analyzed in the AQCS and control groups, respectively. AQCS significantly increased the ADR (0.289 vs 0.165, P < .001) and the mean number of adenomas per procedure (0.367 vs 0.178, P < .001) compared with the control group. A significant increase was also observed in the polyp detection rate (0.383 vs 0.254, P = .001) and the mean number of polyps detected per procedure (0.575 vs 0.305, P < .001). In addition, the withdrawal time (7.03 minutes vs 5.68 minutes, P < .001) and adequate bowel preparation rate (87.34% vs 80.63%, P = .023) were superior for the AQCS group. CONCLUSIONS:AQCS could effectively improve the performance of colonoscopists during the withdrawal phase and significantly increase polyp and adenoma detection. (Clinical trial registration number: NCT03622281.).
RCT Entities:
BACKGROUND AND AIMS: Quality control can decrease variations in the performance of colonoscopists and improve the effectiveness of colonoscopy to prevent colorectal cancers. Unfortunately, routine quality control is difficult to carry out because a practical method is lacking. The aim of this study was to develop an automatic quality control system (AQCS) and assess whether it could improve polyp and adenoma detection in clinical practice. METHODS: First, we developed AQCS based on deep convolutional neural network models for timing of the withdrawal phase, supervising withdrawal stability, evaluating bowel preparation, and detecting colorectal polyps. Next, consecutive patients were prospectively randomized to undergo routine colonoscopies with or without the assistance of AQCS. The primary outcome of the study was the adenoma detection rate (ADR) in the AQCS and control groups. RESULTS: A total of 659 patients were enrolled and randomized. A total of 308 and 315 patients were analyzed in the AQCS and control groups, respectively. AQCS significantly increased the ADR (0.289 vs 0.165, P < .001) and the mean number of adenomas per procedure (0.367 vs 0.178, P < .001) compared with the control group. A significant increase was also observed in the polyp detection rate (0.383 vs 0.254, P = .001) and the mean number of polyps detected per procedure (0.575 vs 0.305, P < .001). In addition, the withdrawal time (7.03 minutes vs 5.68 minutes, P < .001) and adequate bowel preparation rate (87.34% vs 80.63%, P = .023) were superior for the AQCS group. CONCLUSIONS: AQCS could effectively improve the performance of colonoscopists during the withdrawal phase and significantly increase polyp and adenoma detection. (Clinical trial registration number: NCT03622281.).
Authors: David Chen; Clifton Fulmer; Ilyssa O Gordon; Sana Syed; Ryan W Stidham; Niels Vande Casteele; Yi Qin; Katherine Falloon; Benjamin L Cohen; Robert Wyllie; Florian Rieder Journal: J Crohns Colitis Date: 2022-03-14 Impact factor: 10.020