Real-world impact of artificial intelligence on adenoma detection: A cross-sectional study at a single center in Lima, Peru.

INTRODUCTION: The use of artificial intelligence (AI) in endoscopic studies has grown in recent years. The present study evaluates the performance of AI in detecting polyps and adenomas in daily clinical practice. MATERIALS AND METHODS: A cross-sectional study was conducted, in which AI-assisted colonoscopies (AIACs) performed between January 2021 and May 2024 were reviewed. Logistic regression was applied for adenoma detection, based on their characteristics. RESULTS: A total of 1,251 colonoscopies were reviewed. The patients in the AIAC group were older than the control group (59 ± 13 vs. 56 ± 12 years, P < .05). There were no differences between sex, procedure indication, bowel preparation, and procedure time. Regarding the primary aim, the AIAC group had a significantly higher polyp detection rate (58 vs. 52%; P < .05) and non-significantly higher adenoma detection rate (39 vs. 33%; P > .05), compared with the control group. In the analysis of adenoma characteristics, the identification of polypoid adenomas (OR: 1.28; 95% CI: 1.04-1.59), smaller 10 mm (OR: 1.41; 95% CI: 1.14-1.74), and located in the proximal colon (OR: 1.31; 95% CI: 1.05-1.65) was significantly higher in the AIAC group, compared with the control group. CONCLUSIONS: The use of AI in colonoscopies resulted in a non-significant increase in the adenoma detection rate but a significant increase in detecting polypoid adenomas smaller than 10 mm and located in the proximal colon.