Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis

Abstract

Background Colonoscopy 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). Objective The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. Methods A 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. Results A 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). Conclusions With 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 Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786

Advancements in Oncology with Artificial Intelligence—A Review Article

“…In recent years, CADe and computer-aided diagnosis (CADx) systems have been developed to automate polyp detection during colonoscopy and further characterize them. Because of its ability to detect diminutive polyps, real-time AI-aided colonoscopy has a greater ADR than colonoscopy (OR 1.53, 95% CI 1.32–1.77; p < 0.001), derived from a metanalysis data [ 4 , 68 , 69 ]. An AI system, GI Genius, uses green squares to highlight suspicious lesions during a colonoscopy by generating a sound for each marker and displaying it as a video of the endoscopy.…”

Scope of Artificial Intelligence in Gastrointestinal Oncology

“…When using video frames or images, the pooled sensitivity and specificity were higher (92% and 89%, respectively) compared to studies that used still images alone, reporting a pooled sensitivity and specificity of 84% and 87%, respectively, for AI in the detection of colon polyps. Most of the studies were retrospective in design [ 74 ]. The types of AI used ranged from SVM, ANN, and CNN to several modifications of deep learning methods.…”

“…A meta-analysis of seven randomized controlled trials (RCTs) showed a significant increase in the rate of polyp detection when AI was used with colonoscopy compared to colonoscopy alone, with an odds ratio of 1.75 (95% CI: 1.56–1.96, p < 0.001). All studies had a higher polyp detection rate in the AI group than the standard colonoscopy alone group [ 74 ]. Recent studies have also shown considerable promise in the use of AI, especially CNN-based systems in colon capsule endoscopy, to improve rates of colon polyp detection [ 75 , 76 ].…”

“…A pooled analysis of 20 studies that used AI to predict the histology of a polyp detected on colonoscopy revealed a sensitivity and specificity of 94% and 87% with 91% accuracy in detecting an adenomatous polyp. SVM was the most common AI in these studies, followed by DNN, CNN, and other DL methods [ 74 ]. A meta-analysis of seven RCTs also showed an increased detection rate of adenomas when AI was combined with colonoscopy compared to colonoscopy alone with an OR 1.53 (95%CI: 1.32–1.77, p < 0.001).…”

“…The absolute improvement in adenoma detection ranged from 6% to 15.2% for AI compared to colonoscopy alone in the studies. All RCTs utilized ANN- or CNN-based AI systems using video streams from colonoscopy [ 74 ]. Table 3 summarizes select studies assessing role of AI in diagnosis/characterization of polyps and colon cancer by imaging [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 ].…”