PubMed · 11317615
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T W Balfour. 2001. Training for colonoscopy.. https://doi.org/10.1177/014107680109400402
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BACKGROUND: Colorectal cancer remains a leading cause of death despite being largely preventable through polypectomy. AI systems designed to enhance polyp detection during colonoscopy have shown promise, but the extent to which they improve detection of different-sized polyps remains unclear. OBJECTIVE: This study compared the size-stratified efficacy of AI-assisted colonoscopy vs standard colonoscopy using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method, and generated exploratory rankings while acknowledging all cross-platform comparisons are indirect. METHODS: This systematic review and network meta-analysis (NMA) searched PubMed, Embase, Cochrane CENTRAL, and Web of Science from inception to July 25, 2026, supplemented by citation searching. We included randomized controlled trials (RCTs) comparing AI-assisted vs standard colonoscopy in adults (≥18 years of age), reporting mean polyp detection counts stratified by size (≤5 mm, 6-9 mm, and ≥10 mm). Two reviewers screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2.0. We conducted frequentist NMA using the HKSJ method with restricted maximum likelihood estimation, calculated 95% prediction intervals (PIs), and assessed heterogeneity using I2 and τ2. Certainty of evidence was rated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. RESULTS: A total of 13 RCTs (4156 participants) compared 8 AI systems to standard colonoscopy, forming a network without direct AI comparisons. For diminutive polyps (≤5 mm), AI showed a modest advantage (standardized mean difference [SMD] 0.21, 95% CI 0.07 to 0.35, 95% PI -1.12 to 1.54), but substantial heterogeneity (I2=86.6%) and wide PI crossing the null indicated high uncertainty. EndoScreener showed the most consistent evidence (SMD 0.36, 95% CI 0.18-0.54). For small and large polyps, effects were minimal (SMD 0.02, 95% CI -0.02 to 0.06, 95% PI -0.03 to 0.07; SMD 0.01, 95% CI 0.00-0.02, 95% PI -0.01 to 0.03). GRADE certainty was very low for diminutive polyps and low for small and large polyps. Sensitivity analysis excluding Tianjin YuJin did not materially change findings. CONCLUSIONS: AI may modestly enhance diminutive polyp detection, but effects on small and large polyps are minimal, with no platform superiority. Given very low to low certainty, findings are hypothesis-generating. This exploratory NMA provides size-stratified comparisons that can inform future head-to-head trial design. Unlike prior reviews aggregating all polyp sizes, we show the overall AI benefit is driven by diminutive polyp detection, providing a framework for targeted deployment-prioritizing AI for diminutive polyp screening, with limited value for larger lesions. Head-to-head trials are urgently needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD420251266932; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251266932.
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BACKGROUND: The aim of this study was to investigate the relation between clinical experience and performance with regard to colonoscopic procedures performed on the HT Immersion Medical Colonoscopy Simulator. The hypothesis is that the performance of novice, intermediate, and experienced operators is different on simulators, just as it is on real patients. METHODS: 25 Postgraduate doctors were recruited and divided into three groups according to their level of colonoscopic experience. Candidates were asked at random to perform colonoscopy on module 3 or 4 of the HT Immersion Medical Colonoscopy Simulator. Modules 3 and 4 have built-in complex loops, and can demonstrate the candidate's ability either to avoid or undo the loop. Candidates in the first group, termed "novice," each had performed fewer than 10 colonoscopies and included four preregistration house officers (PRHOs), five specialist trainee resistrars (SpRs) and two consultants. This novice group had completed 80 episodes among them. In the second group, termed "intermediate," each candidate had performed between 11 and 100 colonoscopies, and the candidates included five SpRs and two research fellows. This intermediate group had completed 65 episodes. Members of the third group, termed "experienced" each had performed more than 101 colonoscopies, and included one SpR and six consultants. This experienced group had completed 45 episodes between them. A time result of 3,600 s (1 h) was used to denote perforation. RESULTS: The experienced group were shown to perform better than the intermediate group, which in turn performed better than the novice group. The assessment was based on multiple factors including time taken to complete the test, percentage of the colonic mucosa visualized, incidence of colonic perforations, and path length used. The results were highly significant statistically for all these factors ( p < 0.000) except in path length used. CONCLUSIONS: This study demonstrated that operators who differ in terms of their clinical experience and technical ability also differ in their performance of simulated colonoscopy. Thus, the findings have shown the simulator technology to be a powerful discriminator of manipulative skills in colonoscopy. The clinical differences that exist between novices and experts in terms of experience and technical expertise in endoscopic procedures are reflected by statistically significant differences in performance on the simulator. It is therefore reasonable to argue that improving proficiency on the simulator may translate into improvements in clinical practice. This study has validated the use of the HT Medical Immersion Colonoscopy Simulator to discriminate among subjects with different levels of experience.