Artificial intelligence applications in colonoscopy

The majority of AI research in colonoscopy has focused on CADe for colon polyp detection and CADx for polyp diagnosis. Over the last few years, several randomized clinical trials – two in the United States – have shown that CADe significantly improves adenoma detection and reduces adenoma miss rates in comparison to standard colonoscopy. The existing data are summarized in Table 1, focusing on the two U.S. studies and an international meta-analysis.

In comparison, the data landscape for CADx is nascent and currently limited to several retrospective studies dating back to 2009 and a few prospective studies that have shown promising results.^10,11 There is an expectation that integrated CADx also may support the adoption of “resect and discard” or “diagnose and leave” strategies for low-risk polyps. About two-thirds of polyps identified on average-risk screening colonoscopies are diminutive polyps (less than 5 mm in size), which rarely have advanced histologic features (about 0.5%) and are sometimes non-neoplastic (30%). Malignancy risk is even lower in the distal colon.¹² As routine histopathologic assessment of such polyps is mostly of limited clinical utility and comes with added pathology costs, CADx technologies may offer a more cost-effective approach where polyps that are characterized in real-time as low-risk adenomas or non-neoplastic are “resected and discarded” or “left in” respectively. In 2011, prior to the development of current AI tools, the American Society for Gastrointestinal Endoscopy set performance thresholds for technologies supporting real-time endoscopic assessment of the histology of diminutive colorectal polyps. The ASGE recommended 90% histopathologic concordance for “resect and discard” tools and 90% negative predictive value for adenomatous histology for “diagnose and leave,” tools.¹³ Narrow-band imaging (NBI), for example, has been shown to meet these benchmarks^14,15 with a modeling study suggesting that implementing “resect and discard” strategies with such tools could result in annual savings of $33 million without adversely affecting efficacy, although practical adoption has been limited.¹⁶ More recent work has directly explored the feasibility of leveraging CADx to support “leave-in-situ” and “resect-and-discard” strategies.¹⁷

Similarly, while CADe use in colonoscopy is associated with additional up-front costs, a modeling study suggests that its associated gains in ADR (as detailed in Table 1) make it a cost-saving strategy for colorectal cancer prevention in the long term.¹⁸ There is still uncertainty on whether the incremental CADe-associated gains in adenoma detection will necessarily translate to significant reductions in interval colorectal cancer risk, particularly for endoscopists who are already high-performing polyp detectors. A recent study suggests that, although higher ADRs were associated with lower rates of interval colorectal cancer, the gains in interval colorectal cancer risk reduction appeared to level off with ADRs above 35%-40% (this finding may be limited by statistical power).¹⁹ Further, most of the data from CADe trials suggest that gains in adenoma detection are not driven by increased detection of advanced lesions with high malignancy risk but by small polyps with long latency periods of about 5-10 years, which may not significantly alter interval cancer risk. It remains to be determined whether adoption of CADe will have an impact on hard outcomes, most importantly interval colorectal cancer risk, or merely result in increased resource utilization without moving the needle on colorectal cancer prevention. To answer this question, the OperA study – a large-scale randomized clinical trial of 200,000 patients across 18 centers from 13 countries – was launched in 2022. It will investigate the effect of colonoscopy with CADe on a number of critical measures, including long-term interval colon cancer risk.²⁰

Despite commercial availability of regulatory-approved CADe systems and data supporting use for adenoma detection in colonoscopy, mainstream adoption in clinical practice has been sluggish. Physician survey studies have shown that, although there is considerable interest in integrating CADe into clinical practice, there are concerns about access, cost and reimbursement, integration into clinical work-flow, increased procedural times, over-reliance on AI, and algorithmic bias leading to errors.^21,22 In addition, without mandatory requirements for ADR reporting or clinical practice guideline recommendations for CADe use, these systems may not be perceived as valuable or ready for prime time even though the evidence suggests otherwise.^23,24 For CADe systems to see widespread adoption in clinical practice, it is important that future research studies rigorously investigate and characterize these potential barriers to better inform strategies to address AI hesitancy and implementation challenges. Such efforts can provide an integration framework for future AI applications in gastroenterology beyond colonoscopy, such as CADe of esophageal and gastric premalignant lesions in upper endoscopy, CADx for pancreatic cysts and liver lesions on imaging, NLP tools to optimizing efficient clinical documentation and reporting, and many others.

Dr. Uche-Anya is in the division of gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston. Dr. Berzin is with the Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston. Dr. Berzin is a consultant for Wision AI, Medtronic, Magentiq Eye, RSIP Vision, and Docbot.

Corresponding Author: Eugenia Uche-Anya eucheanya@mgh.harvard.edu Twitter: @UcheAnyaMD @tberzin