Impact of Introducing Artificial Intelligence on Colonoscopy: A Retrospective Study on Potential Benefits and Drawbacks
Ayaka Takasu, Hirofumi Kogure, Zhehao Dai, Yuki Yamada, Masako Nakayama, Robert Bechara, Takuji Gotoda, Yoshimasa Mıura
Abstract
BACKGROUND AND AIM: Computer-aided detection (CAD) can improve adenoma detection rates (ADRs); however, the impact of its introduction into real-world practice remains unclear. This study investigated the effect of CAD's introduction on colonoscopy in a hospital. METHODS: This retrospective study included 1314 patients who underwent colonoscopy between January and December 2023 at a single facility where CAD was introduced in three of four endoscopy rooms. ADR, polyp detection rate (PDR), and sessile serrated lesion detection rate (SSLDR) were first compared between patients who underwent colonoscopy without CAD before introduction to the facility (pre-intervention non-CAD group) and those who underwent colonoscopy with CAD after introduction (CAD group). Subsequently, cases without CAD were analyzed to evaluate endoscopists' performance by comparing the detection rates between the pre-intervention non-CAD group and patients who underwent colonoscopy without CAD after introduction (post-intervention non-CAD group). RESULTS: ADR (49.3% vs. 31.6%, p < 0.001) and PDR (57.9% vs. 39.8%, p < 0.001) were significantly higher in the CAD group than in the pre-intervention non-CAD group; SSLDR (4.4% vs. 2.8%, p = 0.14) was comparable between groups. ADR (31.6% vs. 13.5%, p < 0.001) and PDR (39.8% vs. 18.2%, p < 0.001) were significantly lower in the post-intervention non-CAD group than in the pre-intervention non-CAD group. CONCLUSIONS: The introduction of CAD-assisted colonoscopy significantly improved ADR and PDR. However, CAD reliance may lead to lapses in attention toward independent lesion detection by endoscopists. It is essential to consider how CAD should be utilized in clinical practice to maximize its benefits.