DANIEL FREEDMAN

Introduction: Several artificial intelligence (AI) systems for polyp detection during colonoscopy have emerged in the gastroenterology literature and continue to demonstrate significant improvements in quality outcomes. Aims & Methods: This study aimed to assess clinical quality outcomes during white light colonoscopy with and without a novel AI system. Fuji 7000 series colonoscopes were used for all exams. This was a randomized (1:1), controlled, prospective, IRB-approved, monitored trial. This clinical trial occurred at a single ambulatory care endoscopy center. Inclusion criteria included participants ages 40-85 who were previously scheduled to undergo colonoscopy for screening, surveillance, or symptoms. Exclusion criteria included suspected or active inflammatory bowel disease, past colorectal surgery, referral for known polyp removal, pregnancy, Boston bowel prep score below 6, and incomplete colonoscopy. The AI system (DEEP2) has undergone previously published feasibility and safety testing (1). The DEEP2AI system was trained and validated on white light sources, excluding the use of digital continuous chromoendoscopy during withdrawals. Results: Mean age was 62.41 years (SD 10.29), 49% were males. Of 674 colonoscopies performed, significant differences were found in ADR between the two arms of the study, those performed without vs. with AI assistance, (10%, from 27% to 37%. χ2(1)=7.65, P=0.0057, ). Significant differences were also found for APC (P = 0.0017) and PDR (15%, from 33-48%; χ2(1)=16.45, P<0.0001). When evaluated by segment of the colon, the right colon showed the largest ADR and APC differences (p=0.01). The false alert rate (mean=4/exam) was lower than the mean of 25 false alerts reported for two previously approved and available AI systems (2). Withdrawal times with vs. without the system were essentially equivalent, (mean 7.2 minutes, p=NS). Thumbnail frozen images of suspected lesions facilitated decision-making when shown alongside the real-time image, saving time. Endoscopy cuffs and caps were tested and found not to interfere with the effectiveness of the AI system. Seven enrolling physicians had varying baseline ADRs from 25-40% and reported on satisfaction exit surveys unanimous desire for continuing to use the AI system, when available. Qualitative interviews with endoscopy nurses likewise revealed very positive reactions overall to the AI system. Conclusion: The 10% improvement in ADR found when applying this novel AI system for polyp detection is on par with other highly effective systems compared in the literature (3). Specifically, the increased ADR in the right colon suggests that such AI systems can compensate for the Achilles's heel region of colonoscopy, where most interval cancers have been reported. User experience will be critical to the adoption of this technology, and the uniquely low false alert rate of the DEEP2system may contribute to reduced alert fatigue, a likely subject of future studies. Training and validating AI on electronic chromoendoscopy images such as linked color imaging and/or narrow band imaging may also enable future AI systems to be further improved by combining the quality improvement gains of chromoendoscopy and AI. Further aspirations remain that these AI systems may impact confidence in the clearance of colonic adenomas, and thus potentially increase intervals between endoscopies. Additional AI system functionalities may help save time in documentation, including photo and video documentation. Overall, AI-assisted colonoscopy shows great promise at improving colonoscopy outcomes.