Background and Aims: Colonoscopies reduce colorectal cancer incidence and mortality, but challenges remain in detecting all precancerous lesions. Operator fatigue, technique variability, and subtle lesions can lead to missed adenomas, highlighting the need for tools that standardize detection quality. Computer-aided detection (CADe) systems utilize artificial intelligence to enhance lesion detection and improve screening colonoscopy quality. This study evaluates the impact of CADe on key quality and efficiency metrics in a clinical setting. Methods: A single-center retrospective study at an academic tertiary center analyzed 4,028 colonoscopies from October 2022 to December 2023. The CADe system was utilized in 4 of 8 endoscopy suites, creating a CADe and control group. Propensity matching accounted for age, gender, indication and physician. Primary outcomes included adenoma detection rate (ADR) and polyp detection rate (PDR). Adenomas per colonoscopy (APC) and polyps per colonoscopy (PPC) were also measured. Per-polyp analyses were performed as a tertiary outcome. Results: ADR was significantly higher in the CADe group (38.6%) vs control (34.2%) (RR = 1.127, P < 0.05). PDR was significantly higher in the CADe group (67.2%) vs control (59.4%) (RR = 1.130, P < 0.05). APC was significantly higher in the CADe group (M = 0.406, SD 0.637) vs control (M = 0.359, SD 0.599) (RR = 1.131, P < 0.05). PPC was significantly higher in CADe group (M = 1.297, SD 1.188) vs control (M = 1.046, SD 1.002) (RR = 1.240, P < 0.05). The increase in polyp resection with CADe was driven predominantly by ≤5 mm lesions, with comparatively smaller differences for larger polyps. Conclusion: Implementing CADe in clinical practice significantly improved ADR, PDR, APC, and PPC. However, the increase in ADR appears inflated by the detection of polyps ≤ 5 mm, whose clinical significance in reducing CRC remains uncertain.
Nayar et al. (Thu,) studied this question.