An artificial intelligence (AI)–based computer-aided polyp detection (CADe) system missed fewer adenomas, polyps, and sessile serrated lesions and identified more adenomas per colonoscopy than a high-definition white light (HDWL) colonoscopy, according to findings from a randomized study.
While adenoma detection by colonoscopy is associated with a reduced risk of interval colon cancer, detection rates of adenomas vary among physicians. AI approaches, such as machine learning and deep learning, may improve adenoma detection rates during colonoscopy and thus potentially improve outcomes for patients, suggested study authors led by Jeremy R. Glissen Brown, MD, of the Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, who reported their trial findings in
The investigators explained that, although AI approaches may offer benefits in adenoma detection, there have been no prospective data for U.S. populations on the efficacy of an AI-based CADe system for improving adenoma detection rates (ADRs) and reducing adenoma miss rates (AMRs). To overcome this research gap, the investigators performed a prospective, multicenter, single-blind randomized tandem colonoscopy study which assessed a deep learning–based CADe system in 232 patients.
Individuals who presented to the four included U.S. medical centers for either colorectal cancer screening or surveillance were randomly assigned to the CADe system colonoscopy first (n = 116) or HDWL colonoscopy first (n = 116). This was immediately followed by the other procedure, in tandem fashion, performed by the same endoscopist. AMR was the primary outcome of interest, while secondary outcomes were adenomas per colonoscopy (APC) and the miss rate of sessile serrated lesions (SSL).
The researchers excluded 9 patients, which resulted in a total patient population of 223 patients. Approximately 45.3% of the cohort was female, 67.7% were White, and 21% were Black. Most patients (60%) were indicated for primary colorectal cancer screening.
Compared with the HDWL-first group, the AMR was significantly lower in the CADe-first group (31.25% vs. 20.12%, respectively; P = .0247). The researchers commented that, although the CADe system resulted in a statistically significantly lower AMR, the rate still reflects missed adenomas.
Additionally, the CADe-first group had a lower SSL miss rate, compared with the HDWL-first group (7.14% vs. 42.11%, respectively; P = .0482). The researchers noted that their study is one of the first research studies to show that a computer-assisted polyp detection system can reduce the SSL miss rate. The first-pass APC was also significantly higher in the CADe-first group (1.19 vs. 0.90; P = .0323). No statistically significant difference was observed between the groups in regard to the first-pass ADR (50.44% for the CADe-first group vs. 43.64 % for the HDWL-first group; P = .3091).
A multivariate logistic regression analysis identified three significant factors predictive of missed polyps: use of HDWL first vs. the computer-assisted detection system first (odds ratio, 1.8830; P = .0214), age 65 years or younger (OR, 1.7390; P = .0451), and right colon vs. other location (OR, 1.7865; P = .0436).
According to the researchers, the study was not powered to identify differences in ADR, thereby limiting the interpretation of this analysis. In addition, the investigators noted that the tandem colonoscopy study design is limited in its generalizability to real-world clinical settings. Also, given that endoscopists were not blinded to group assignments while performing each withdrawal, the researchers commented that “it is possible that endoscopist performance was influenced by being observed or that endoscopists who participated for the length of the study became over-reliant on” the CADe system during withdrawal, resulting in an underestimate or overestimation of the system’s performance.
The authors concluded that their findings suggest that an AI-based CADe system with colonoscopy “has the potential to decrease interprovider variability in colonoscopy quality by reducing AMR, even in experienced providers.”
This was an investigator-initiated study, with research software and study funding provided by Wision AI. The investigators reported relationships with Wision AI, as well as Olympus, Fujifilm, and Medtronic.