Harnessing the potential of artificial intelligence‐assisted colonoscopy to reduce the risk of post‐colonoscopy interval colorectal cancer (2023)

Most cases of interval post-colonoscopy colorectal cancer (PCCRC) are thought to be due to missed cancers and missed or incompletely resected adenomas.1 A retrospective study of 107 PCCRC in England reported that 27% of PCCCR could be categorized as “possible missed lesion, prior examination adequate” and 58% of PCCRC categorized as “possible missed lesion, prior examination inadequate.”2 This issue of PCCRC highlights the importance of meticulous examination during colonoscopy to reduce the risk of missed lesions. Artificial intelligence (AI)-assisted colonoscopy is now available for routine clinical use. AI-assisted colonoscopy has been shown to improve adenoma detection rate (ADR) regardless of the level of expertise of the endoscopists.3 ADR is used as a quality indicator for colonoscopy, and a higher ADR is associated with a lower risk of PCCRC.4 However, ADR cannot quantify the quality of the colonoscopy beyond the detection of the first adenoma and does not provide indication of whether additional adenomas are missed. Studies that evaluated endoscopists across a range of ADR reported adenoma miss rates (AMR) that range between 15% and 24%.5 Hence, in order to better clarify the potential ability of AI-assisted colonoscopy to reduce the risk of PCCRC, it is important to analyze its impact on both ADR and AMR. In this issue of Journal of Gastroenterology and Hepatology, Shah et al. reported the results of a systematic review and meta-analysis (MA) conducted to determine the effect of AI-assisted colonoscopy on AMR and ADR based on 14 randomized controlled trials (RCT) with a total of 10 928 subjects.5 AI-assisted colonoscopy resulted in 65% reduction in AMR (OR 0.35; 95% CI: 0.25–0.49, P < 0.001), 78% reduction in the sessile serrated lesion miss rate (OR 0.22; 95% CI: 0.08–0.65, P < 0.01), and 52% increase in ADR (OR 1.52; 95% CI: 1.39–1.67, P = 0.04) when compared with control. This is a well conducted MA that evaluated the pooled effect of AI systems on AMR using data from RCT. It has the power to perform further subgroup analysis to clarify the impact of AI in the detection of specific types and size of adenomas. Although there is a lack of uniformity in the types of AI systems that were included in the MA, with some focused on procedural adequacy and others focused on lesion detection, this does not detract from the core message about the value of AI-assisted colonoscopy in achieving clinically relevant outcomes. Another limitation is that only two of the included studies were international multicenter studies. However, the fact that the individual studies conducted in Asia, Europe, and the United States all consistently demonstrated the effectiveness of AI-assisted colonoscopy would suggest that despite this limitation, the results are probably applicable globally. Setting up an AI-assisted colonoscopy service does entail additional costs, as an additional software system must to be purchased and installed together with the endoscopy system. Some of the commercially available AI software can be used across different endoscopy platforms, whereas others are endoscopy system specific. From a strategic perspective, if AI-assisted colonoscopy is incorporated as part of a national CRC screening program, it may be cost-effective. A Markov model microsimulation of using colonoscopy with and without AI for CRC screening of average risk individuals in the United States, compared with no screening, was recently published. In this simulation model, AI-assisted colonoscopy contributed to reduction of CRC incidence and mortality with a sustainable cost-saving profile. The relative reduction in CRC incidence with AI-assisted colonoscopy (48.9%) was higher than that without AI (44·2%). The relative reduction in CRC mortality with AI-assisted screening colonoscopy (52·3%) was also higher than that without AI (48·7%). AI decreased the discounted costs per screened individual from US$3400 to US$3343. At the population level, the implementation of AI-assisted colonoscopy resulted in yearly additional prevention of 7194 CRC and 2089 CRC-related deaths, and a yearly cost saving of US$290 million.6 There remains areas that merit further investigation. AI helps to focus attention on lesions within the endoscopic view that are missed by the endoscopist. On the other hand, a distal attachment, such as Endocuff Vision, improves visualization by flattening mucosal folds and exposing hidden adenomas. A recent RCT with 1813 subjects reported that Endocuff Vision significantly increased ADR (47.8% vs 40.8%; RR 1.17, 95% CI: 1.06–1.30) in a CRC screening program where patients underwent colonoscopy following a positive fecal immunochemical test.7 A network MA had reported that AI-assisted colonoscopy achieved higher ADR than the use of distal attachments.8 There are currently no published data on the combined use of AI-assisted colonoscopy and distal attachments, which logically would be synergistic and should further increase ADR. However, there is an ongoing RCT (NCT05133544) on the use Endocuff with or without AI-assisted colonoscopy in the detection of colorectal adenoma. We look forward to the results of this RCT as well as other similar studies. The detection of dysplasia in longstanding inflammatory bowel disease (IBD) remains a clinical challenge despite the use of dye-based and virtual chromoendoscopy. Currently available AI systems for polyp detection were not developed in IBD patient populations. Dysplasia in long-standing colitis has a flatter macroscopic morphology compared with sporadic adenomas and can be further obscured by the presence of extensive post-inflammatory polyps. Hence, the utility of the currently available AI-assisted colonoscopy in the detection of dysplasia in IBD is unclear and requires further research and development.9 The data on the direct impact of AI-assisted colonoscopy in reducing the risk of PCCRC are not yet available. However, AMR and ADR are both important surrogate markers that highlight the effectiveness of AI-assisted colonoscopy. Therefore, we should harness the potential of AI to reduce the risk of PCCRC and the incidence of CRC. Colonoscopy is but one component of the CRC screening program. Even as we make strides in the field of endoscopy, other components of the screening program, such as improving uptake of screening tests, ensuring timeliness of follow-up actions for positive results, and adherence to appropriate time intervals for surveillance colonoscopy, are also important and must not be neglected. Ultimately all screening programs will need to be adjusted based on national needs and available resources. Nil.