Abstract
BackgroundColonoscopy reduces the incidence of colorectal cancer (CRC) by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR).ObjectiveThe aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps.MethodsA comprehensive literature search was undertaken using the databases of Embase, MEDLINE, and the Cochrane Library. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling.ResultsA total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53, 95% CI 1.32-1.77; P<.001).ConclusionsWith the aid of machine learning, there is potential to improve ADR and, consequently, reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians.Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786
Highlights
Colorectal cancer (CRC) is the third-leading malignancy worldwide and a leading cause of mortality [1]
colorectal cancer (CRC) typically develops from sporadic colorectal adenomatous polyps, and colonoscopy is established for the detection and resection of these lesions, which has been shown to reduce the incidence and mortality from CRC [2]
The meta-analysis showed a significant increase in pooled polyp detection rate Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) (PDR) in patients with the use of artificial intelligence (AI) for polyp detection during colonoscopy compared with patients who had standard colonoscopy
Summary
Colorectal cancer (CRC) is the third-leading malignancy worldwide and a leading cause of mortality [1]. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR). Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. This is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786
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