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EFFECT OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION TO THE LATEST GENERATION 4K COLONOSCOPY

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Introduction

Colonoscopy is an acclaimed screening test to detect colorectal cancer. The most important quality indicators for colonoscopy are adenoma detection rate (ADR), cecal intubation rate (CIR), withdrawal time (WT), and bowel preparation (BBPS). In modern endoscopy practice, the human eye is enhanced by high-definition 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 may be the next step to improve the quality of tests performed.

Aims & Methods

The aim was to assess the effect of AI implementation in the environment of the latest generation of endoscopes and 4K visualisation in retrospective analysis. In this study Olympus Endo-Aid CADe artificial intelligence system was used, togerher with the latest X1 series endoscope set using LED lighting and 4K ultra high-resolution technology. The study included patients over 18 years old who underwent a colonoscopy for various indications in 5 years. 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. The local bioethical committee approved the study following the applicable requirements.

Results

A total of 2,000 participants were included in the analysis, divided into two groups regarding AI implementation. The overall polyp detection rate (PDR) was similar regarding CADe implementation (AI: 46.7% vs. non-AI: 44.9%. p=0.419). Both the Adenoma Detection Rate (29.7 vs. 28.9%, p=0.694) and Advanced Adenoma Detection Rate (6.9 vs. 7.1%, p=0.861) changed unremarkably. However, we noted a significant elevation in The Mean Polyp per Patient score (MPP). The MPP raised 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 vs. 18.0%, p<0.001), with no difference in the other segments and other parameters. Investigating the MPP separately in each segment showed a significant difference for the right colon (0.33 vs. 0.23, p=0.032) and the left colon (0.47 vs. 0.28, p<0.001), with no difference in the other segments. When adjusted to bowel preparation the PDR and MPP were constantly higher in the AI group (29.3 vs. 19.0%, p<0.001, and 0.48 vs. 0.30, p<0.001, respectively). In addition, the significant impact of AI implementation on MPP faded in the right colon (0.33 vs. 0.24, p=0.051) when compared with the overall analysis.

Table 1. The characteristics of quality indicators for colonoscopy between groups

Group	AI	Non-AI	P-value
PDR, %	46.7	44.9	0.419
ADR, %	29.7	28.9	0.694
AADR, %	6.9	7.1	0.861
MPP, %	1.26	0.85	<0.001

Conclusion

Although recently published evidence is optimistic regarding AI efficiency in improving the quality of colonoscopy, the results we provided widen the overall perspective. Prospective randomized controlled trials (RCTs) should elucidate the role of artificial intelligence in colonoscopy.

References

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