Computer-aided colonoscopies improved adenoma detection rates

Using artificial intelligence (AI) in colonoscopy proved beneficial in terms of higher adenoma detection rates and lower rates of missed adenomas in comparison with the conventional diagnostic procedure. The computer-aided detection was particularly helpful in the discovery of small and very small adenomas.

“Up to a fourth of all adenomas are missed during colonoscopy, which potentially results in post-colonoscopy colorectal cancers,” Dr Michiel Maas (Raboud UMC, the Netherlands) said [1]. To further explore possibilities to improve this situation, Dr Maas and colleagues designed an international, multicentre, randomised-tandem trial to evaluate the potential enhancement of polyp detection by using AI in colonoscopy. In 2 different groups, 916 patients with a mean age of about 60 years underwent a colonoscopy with or without use of the MAGENTIQ-COLO™ study device (magentiq.com). “A subset of patients was further randomised to undergo a tandem colonoscopy, so either a conventional colonoscopy followed by AI colonoscopy or vice versa,” Dr Maas explained. The primary outcome measure was adenoma per colonoscopy (APC). Secondary objectives were the adenoma detection rate (ADR) and adenoma miss rate. Reasons for the colonoscopy in the conventional group and computer-aided detection group were surveillance (44.2% and 43.1%, respectively) and non-immunological faecal occult blood test (55.8% and 56.9%, respectively).

The results showed a statistically significant APC rate for the computer-aided detection compared with the conventional group (0.70 vs 0.51; P=0.014). The ADR was also superior with computer-aided detection; not only in the entire cohort (37% vs 30%; P=0.014) but also for both indications: surveillance (P=0.001) and non-immunological faecal occult blood test (P=0.014). Dr Maas conveyed that the adenoma miss rate was almost halved with computer-aided detection (19%) in comparison with conventional colonoscopy (36%), while withdrawal times without intervention were equal in both groups.

In terms of adenoma characteristics that aided in better detection with computer-aided detection, size mattered. “We found an increased detection of diminutive adenomas sized ≤5 mm, but we also found an increase in small adenomas sized 6–9 mm in the computer-aided detection group compared with the conventional group. There were no differences in the detection of advanced adenomas or sessile serrated lesions,” Dr Maas stated. Furthermore, computer-aided detection found more adenomas in the proximal colon (P=0.006).

“This study further emphasises the beneficial role of AI or computer-aided detection in improving our detection rates in a regular screening and surveillance population,” Dr Maas concluded.

1. Maas MHJ, et al. A novel computer-aided polyp detection system in daily clinical care: an international multicentre, randomised, tandem trial. LB06, UEG Week 2022, 8–11 October, Vienna, Austria.

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Posted on 2 December, 202228 March, 2024