Effect of artificial intelligence implementation to the latest generation 4K colonoscopy.

<b>Indroduction:</b> Colonoscopy is an acclaimed screening test to detect colorectal cancer (CRC). The most important quality indicators for colonoscopy are adenoma detection rate (ADR), cecal intubation rate (CIR), withdrawal time (WT), and bowel preparation (Boston Bowel Preparation Scale; BBPS). In modern endoscopy practice, the human eye is enhanced by highdefinition white-light visualization and advanced imaging technology. The main limitation of this procedure is the detection rate of suspicious lesions. The next generation of endoscopes with 4K resolution and computer-aided detection (CADe) based on artificial intelligence (AI) may be the next step to improve the quality of tests performed.<b>Aim:</b> The aim was to assess the effect of CADe implementation in the environment of the latest generation of endoscopes and 4K visualization in retrospective analysis.<b>Methods:</b> The study included 2,000 patients over 18 years old who underwent colonoscopy for various indications. Olympus Endo-Aid CADe AI system was used, together with the latest X1 series endoscope set using LED lighting and 4K ultra high-resolution technology. Group I consisted of 1,000 consecutive tests performed using Endo-Aid CADe, and group II the first 1,000 consecutive tests without the CADe system. ADR, Advanced adenoma detection rate (AADR), polyp detection rate (PDR), and mean polyp per patient score (MPP) were assessed in each group<b>Results:</b> A total of 2,000 participants were included in the analysis, divided into two groups regarding CADe implementation. The overall PDR was similar in the analyzed groups (AI: 46.7% <i>vs.</i> non-AI: 44.9%, P = 0.419). Both ADR (29.7 <i>vs.</i> 28.9%, P = 0.694) and AADR (6.9 <i>vs.</i> 7.1%, P = 0.861) changed unremarkably. However, a significant elevation in MPP was noted. The MPP rose from 0.85 in the non-AI group to 1.26 in the AI group (P<0.001). The comparative analysis conducted separately for each segment of the bowel revealed that PDR remarkably increased in the left colon (29.3 <i>vs.</i> 18.0%, P<0.001), with no difference for other segments and other parameters. Investigating the MPP separately in each segment showed a significant difference for the right colon (0.33 <i>vs.</i> 0.23, P = 0.032) and the left colon (0.47 <i>vs.</i> 0.28, P<0.001). When adjusted to bowel preparation the PDR and MPP were constantly higher in the AI group (29.3 <i>vs.</i> 19.0%, P<0.001, and 0.48 <i>vs.</i> 0.30, P<0.001, respectively). In addition, the significant impact of AI implementation on MPP faded in the right colon (0.33 <i>vs.</i> 0.24, P = 0.051) when compared with the overall analysis.<b>Conclusions:</b> Although recently published evidence is optimistic regarding AI efficiency in improving the quality of colonoscopy, the provided results widen the overall perspective. Prospective randomized controlled trials (RCTs) including procedures performed with newest generation scopes should elucidate the role of AI in high-resolution colonoscopy.