Adenoma miss rates were significantly lower with the use of an artificial intelligence (AI)–based computer-aided detection (CADe) system than with high-definition white light (HDWL), according to a new prospective, multicenter, single-blind randomized study based on data from more than 200 colonoscopies.
Missed adenomas can be generally categorized as adenomas fully obscured from the visual field or those appearing partly or fully in the visual field but missed by an endoscopist, wrote Jeremy R. Glissen Brown, MD, of Harvard Medical School, Boston, and colleagues. While retrospective and prospective studies in China, Italy, and Japan have shown that deep-learning CADe improves adenoma identification during colonoscopy, there have been no prospective U.S. studies on CADe in a diverse population, they noted.
In the study published in Clinical Gastroenterology and Hepatology, the researchers reviewed data from 223 adults aged 22 years and older who underwent screening colonoscopies across four U.S. academic medical centers between 2019 and 2020. The procedure indication was primary colorectal cancer screening for 59.6% of the patients and postpolypectomy surveillance for 40.4%. Among this cohort, 45.3% (101) were female, 67.7% (151) were White, and 21% (133) were African American. Participants were randomized to receive either CADe colonoscopy first or HDWL colonoscopy first; the patients immediately underwent the other procedure in tandem fashion from the same endoscopist.
The primary outcome of the study was adenoma miss rate (AMR), defined as “the number of histologically confirmed adenomas detected during the second colonoscopy in either arm divided by the total number of adenomas detected during both procedures.” Sessile serrated lesion (SSL) miss rates and adenomas per colonoscopy (APC) were secondary outcomes.
Overall, the primary outcome of AMR was significantly lower in the CADe-first group, compared with the HDWL-first group (20.12% vs. 31.25%; P = .0247), with an odds ratio of 1.8048 (95% CI, 1.0780-3.0217). The CADe-first group yielded a lower SSL miss rate, compared with the HDLW-first group (7.14% vs. 42.11%; P = .0482), as well as a lower polyp miss rate (20.70% vs. 33.71%; P = .0007). The first-pass number of APC was significantly higher in the CADe-first group, compared with the HDWL-first group (1.19 [SD 2.03] vs. 0.90 [SD 1.55]; P = .0323). In addition, the first-pass adenoma detection rate (ADR) was not significantly different in the CADe-first group, compared with the HDWL-first group (50.44% vs. 43.64%; P = .3091), and the median withdrawal time was significantly shorter with CADe, compared with HDWL (9.5 minutes vs. 8.5 minutes; P = .0098).
There were no significant observable differences between the two groups regarding missed adenomas arranged by size or location. Moreover, there were no significant differences in miss rates for hyperplastic polyps or advanced adenomas. Factors significantly associated with missed adenomas included being in the HDLW-first group, age 65 years or younger, and the right colon vs. other locations. No immediate adverse events occurred in either group.
According to the researchers, while previous studies in China and Italy have shown increased ADR using CADe systems, these results are not generalizable to the U.S. population for several reasons, notably the studies’ inclusion of colonoscopy indications other than colorectal cancer screening and surveillance. Though the present study showed a significantly lower AMR with CADe, it still represents missed adenomas. The researchers note: “In the present study, in which CADe detected 285 polyps, there were only three false negatives (defined as polyps that were visualized by the endoscopist but not by the CADe system). Overall, this suggests that the ‘missed polyps’ in the CADe arm may have been obscured behind folds rather than in the visual field.” They added, “Further research is needed on combining CADe technologies with mucosal exposure devices, as the benefits of these tools for polyp detection may be additive.”
The study findings were limited by several factors, including the inability to detect a difference in overall ADR, the limited generalizability of the tandem study design to real-world practice, the inclusion of only experienced endoscopists, and the use of a second monitor that may have impacted gaze patterns, the researchers noted. However, the results represent the first examination of deep-learning CADe in a diverse U.S. population and showed a decrease in adenoma miss rates and decreased miss rates for polyps and SSLs, compared with HDWL. Based on these findings, the authors concluded CADe “has the potential to decrease inter-provider variability in colonoscopy quality by reducing adenoma miss rate even in experienced providers.”
Reducing Miss Rates Matters
“Missed adenomas can be associated with the development of interval colorectal cancer, so whether novel technologies such as artificial intelligence-based computer-aided polyp detection system can decrease adenoma miss rate is of interest,” said Atsushi Sakuraba, MD, of the University of Chicago, in an interview.
Dr Sakuraba said he was not surprised by the current study findings, as several pilot and randomized studies have shown the benefits of AI-based polyp detection systems. As for how the AI-assisted technology might improve practice, he said it may be a valuable addition. “Adenoma miss rate was significantly lower with an AI-based polyp detection system, so it might lead to decreased colorectal cancer,” he explained. “Various methods to improve adenoma detection should complement each other.
Sakuraba also commented that additional research is needed outside of academic centers, noting “further studies in the community setting involving various endoscopists are required to confirm generalizability.”
Lead author Glissen Brown had no financial conflicts to disclose. This was an investigator-initiated study, with research software and study funding provided by Wision. Sakuraba disclosed collaborative research with Fuji film, which was not involved in this study.
This article originally appeared on MDedge.com, part of the Medscape Professional Network.