[Effect of an artificial intelligence-assisted recognition system on colonoscopy quality]

Objective: To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. Methods: From January 2023, the data on 700 patients who underwent colonoscopy in the Digestive Endoscopy Center of the First Affiliated Hospital of Zhejiang Chinese Medical University were collected prospectively. Based on a computerized number method, patients were divided into the AI assistance group and control group. The detection rate of adenomas (ADR) and polyps, number and size of adenomas, Boston bowel preparation scale (BBPS), intubation time, withdrawal time, and cecal intubation rate were compared between groups. Normally distributed data were analyzed with the t-test for independent samples. Non-normally distributed data were analyzed with the Rank sum test. Categorical data were analyzed with the Chi-square test. Results: In total, 691 patients were included in the analysis. According to the intention to treat (ITT) analysis and per-protocol (PP) analysis, the withdrawal time of the AI group was higher than that of the control group (ITT:436 (305, 620) vs 368 (265, 510) s, Z=-4.24, P<0.001;PP:439 (306, 618) vs 364 (262, 500) s,t=-4.50, P<0.001); however, there were no significant differences in the ADR (ITT:123(35.5%) vs 111(32.2%), χ²=0.88, P=0.349;PP:108(34.2%) vs 99(31.1%), χ²=0.67, P=0.414), the number of adenomas (ITT:0(0, 1) vs 0(0, 1),Z=-1.08, P=0.282;PP:0(0, 1) vs 0(0, 1),Z=-0.87, P=0.387), the polyp detection rate (ITT:85(24.6%) vs 85(24.6%),χ²=0.001, P=0.983;PP:79(25.0%) vs 77(24.2%),χ²=0.05, P=0.818), BBPS (ITT:6.5±0.9 vs 6.5±0.7,t=-0.59, P=0.555;PP:6.7±0.6 vs 6.6±0.6,t=-1.83, P=0.068), and cecal intubation rate (ITT:346(100.0%) vs 343(99.4%), χ²=0.50, P=0.478) between these two groups. After excluding inadequate bowel preparation and failed cecal intubation cases, the AI-assisted system was found to significantly improve the detection rate of small adenomas (≤5 mm) (PP:27.8%(88/316)vs 21.1%(67/318), χ²=3.94, P=0.047). Conclusions: The application of an AI-assisted system in colonoscopy can increase the withdrawal time and improve the detection rate of small adenomas.