Adenoma Detection Rate in Artificial Intelligence-assisted Colonoscopy Performed by Endoscopists with Different Levels of Experience - a Quasi Randomized Controlled Multicenter Trial

Colorectal cancer (CRC) is the third most common cancer, and the second most common cause of cancer-related death worldwide. CRC screening is used for detection and removal of precancerous lesions before they develop into cancer. Colonoscopy is regarded being superior to other screening tests, and is therefore used as the golden standard.

Screening colonoscopy is associated with a reduced risk of CRC-related death. Since it is not possible for an endoscopist to determine the histopathology of the polyp with certainty during a colonoscopy, detected pre-malignant lesions should be removed and sent for histological examination. Multiple studies have shown that there is a strong association between findings at the baseline screening colonoscopy and rate of serious lesions at the follow up colonoscopy. Risk factors for adenoma, advanced adenoma and cancer at follow-up colonoscopy are multiplicity, size, villousness, and high degree dysplasia of the adenomas at the baseline screening colonoscopy.

The adenoma detection rate (ADR) is the percentage of examinations performed by one endoscopist, in which one or more adenomas are found. This is widely accepted as the main quality indicator for each endoscopist and colonoscopy. There is strong evidence that the ADR is inversely correlated to the incidence of interval CRC. With each 1,0% increase in the ADR there is a 3,0% decrease in the risk of developing CRC. Unfortunately, adenomas and advanced adenomas are frequently missed, and the ADR varies widely among different endoscopists. Also, the quality changes throughout the day. Both the withdrawal time and the ADR decreases by the end of the day, approximately by 20% and 7% respectively. Small improvements in the colonoscopy quality may have great importance for the outcome when screening for CRC.

Artificial intelligence (AI) can reduce the performance variability by working as a pair of additional virtual eyes, compensating for perceptual errors due to fatigue, distraction and inaccurate human vision. Within the last few years there have been published several randomized controlled trials (RCT) investigating the efficacy of real time computer-aided detection. Among these, all of the RCT´s which have ADR as the primary outcome, have shown that the use of AI contributes to a significantly higher ADR, compared colonoscopies without assistance of an AI system.

Repici et al. have shown that experience of the endoscopist only plays a minor role as a determining factor. Correspondingly, results from a previous study by Liu et al. indicates that CADe systems are not only useful for endoscopists with a low detection rate, but can also increase the ADR for more experienced endoscopists. Kamba et. al reports a significant lower adenoma miss rate (AMR) for CADe-assisted colonoscopy, compared to a conventional colonoscopy. This is independent on the endoscopist´s level of expertise. Other studies conclude that AI probably will benefit the less experienced endoscopists more. However, there are only a limited number of studies investigating the impact of AI when used by less experienced endoscopists.