Abstract
BACKGROUND AND AIMS: 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. This study assesses clinical quality outcomes during white-light colonoscopy with and without a novel AI computer-aided detection system, DEtection of Elusive Polyps (DEEP(2)), using Fuji 7000 series colonoscopes (Fujifilm, Singapore). METHODS: An unblinded, randomized (1:1), controlled, prospective study was performed at a single ambulatory care endoscopy center under institutional review board approval. Included participants ages 40 to 85 years were scheduled to undergo colonoscopy for screening, surveillance, or symptoms. Exclusion criteria were inflammatory bowel disease, prior colorectal surgery, known polyp referral, pregnancy, inadequate bowel prep, and incomplete colonoscopies. DEEP(2) was trained and validated only on white-light imaging, excluding the use of continuous digital chromoendoscopy. RESULTS: Mean patient age was 62.4 years (SD, 10.29), and 49% were men. Of 674 colonoscopies analyzed, significant differences were found in the adenoma detection rate (ADR) between the 2 arms of the study, those performed without versus with DEEP(2) (10% vs 27%-37%, respectively; P = .0057). Significant differences were also found for adenomas per colonoscopy (APCs; .62 vs .39, respectively; P < .001) and polyp detection rate (17% vs 39%-56%, respectively; P < .001). In the right-sided colon, where most interval cancers are found, it also showed significant ADR and APC differences (P < .01). The false alert rate (mean, 4 per examination) was lower than the mean of >20 false alerts reported for other computer-aided detection systems. Withdrawal times were equivalent between arms (mean, 7.2 minutes; not significant). CONCLUSIONS: Seven enrolled physicians and 5 participating nurses reported a unanimous desire to continue using DEEP(2) after the completion of the study and after commercial availability. (Clinical trial registration number: MYTRIALS.).