Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis

Abstract

Background: Colonoscopy can detect colorectal adenomas and reduce the incidence of colorectal cancer, but there are still many missing diagnoses. Artificial intelligence-assisted colonoscopy (AIAC) can effectively reduce the rate of missed diagnosis and improve the detection rate of adenoma, but its clinical application is still unclear. This systematic review and meta-analysis assessed the adenoma missed detection rate (AMR) and the adenoma detection rate (ADR) by artificial colonoscopy. Methods: Conduct a comprehensive literature search using the PubMed, Medline database, Embase, and the Cochrane Library. This meta-analysis followed the direction of the preferred reporting items for systematic reviews and meta-analyses, the preferred reporting item for systematic review and meta-analysis. The random effect model was used for meta-analysis. Results: A total of 12 articles were eventually included in the study. Computer aided detection (CADe) significantly decreased AMR compared with the control group (137/1039, 13.2% vs 304/1054, 28.8%; OR,0.39; 95% CI, 0.26–0.59; P < .05). Similarly, there was statistically significant difference in ADR between the CADe group and control group, respectively (1835/5041, 36.4% vs 1309/4553, 28.7%; OR, 1.54; 95% CI, 1.39–1.71; P < .05). The advanced adenomas missed rate and detection rate in CADe group was not statistically significant when compared with the control group. Conclusions: AIAC can effectively reduce AMR and improve ADR, especially small adenomas. Therefore, this method is worthy of clinical application. However, due to the limitations of the number and quality of the included studies, more in-depth studies are needed in the field of AIAC in the future.

Improvement of adenoma detection rate by two computer-aided colonic polyp detection systems in high adenoma detectors: a randomized multicenter trial

“…Advancements in artificial intelligence technology over the past decade have opened up new possibilities for overcoming human error during colonoscopy procedures [6]. Recent metaanalyses provide abundant evidence demonstrating the value of a computer-aided polyp detection (CADe) system by increasing the ADR and reducing missed adenomas [7,8,9,10]. Despite the impressive results, many studies have been criticized for having a low baseline ADR, raising the question of whether similar results would have been obtained had the baseline ADR been higher [6,7,8,9].…”

“…Recent metaanalyses provide abundant evidence demonstrating the value of a computer-aided polyp detection (CADe) system by increasing the ADR and reducing missed adenomas [7,8,9,10]. Despite the impressive results, many studies have been criticized for having a low baseline ADR, raising the question of whether similar results would have been obtained had the baseline ADR been higher [6,7,8,9]. Furthermore, previous meta-analyses concluded that CADe could aid in detecting only diminutive polyps while not increasing the detection of advanced adenoma [11,12].…”

Artificial intelligence and the push for small adenomas: all we need?

“…They all show a more or less significant increase in detection of small adenomas. The same results are summarized over and over again in no less than 16 meta-analyses [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], plus a protocol for a further one [36], as well as a meta-analysis of some of these meta-analyses [37]! "The question relates to the ultimate clinical relevance of increasing ADR from high to even higher levels …, we still do not know whether we have a cutoff ADR level, above which there is no further improvement in cancer prevention.…”