Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures

Table 1 PICO (Population, Intervention, Comparison, Outcome) framework

Component	Definition	Criteria for inclusion	Criteria for exclusion
Research question	What is the diagnostic accuracy and clinical impact of AI-based real-time histology prediction for colorectal polyps compared to conventional histopathology and endoscopists?	Studies must specifically investigate AI assisted systems for real time polyp histology prediction	Studies that do not focus on real-time AI applications in live colonoscopy settings
Population	Patients undergoing colonoscopy with colorectal polyp detection and histology prediction	(1) Adults (≥ 18 years) undergoing colonoscopy; (2) Studies involving patients with colorectal polyps (adenomatous, hyperplastic, sessile serrated); and (3) Human subjects (no in vitro or animal studies)	(1) Studies focusing on animal models, in vitro, or simulation-based research; and (2) Pediatric studies (patients < 18 years)
Intervention	AI-based systems for real-time histology prediction of colorectal polyps	(1) AI assisted colonoscopy systems for polyp detection and classification; (2) Machine learning and deep learning models (e.g., convolutional neural networks); and (3) AI enhanced imaging techniques (e.g., narrow-band imaging, endocytoscopy)	AI models used only for retrospective analysis (not real-time)
Comparison	Standard histopathological methods or expert endoscopists’ assessments	(1) Histopathological examination as the gold standard; (2) Comparison with experienced endoscopists’ accuracy; and (3) Conventional endoscopy methods without AI assistance	AI models compared only with other AI models (without human or histological reference)
Outcome	Diagnostic accuracy of AI systems in polyp histology prediction	Primary outcomes: (1) Sensitivity, specificity, accuracy, and negative predictive value of AI models; and (2) Adenoma detection rate. Secondary outcomes: (1) Reduction in unnecessary polypectomies; (2) Interobserver variability between AI and human experts; and (3) Time efficiency and cost-effectiveness of AI-assisted endoscopy	(1) Studies with incomplete or insufficient clinical validation of AI performance; and (2) Studies that do not report key diagnostic accuracy metrics (e.g., missing sensitivity, specificity, or adenoma detection rate)

AI: Artificial intelligence.

Table 2 Summary of included studies

Ref.	Country	Study design and setting	Total lesion	Patient population description	AI intervention description	Comparison group	AI correct diagnoses	Human correct diagnoses
Barua et al[38]	United Kingdom	Randomized controlled trial	359	Screening colonoscopy in general practice	AI-assisted white-light and narrow band imaging	Experienced endoscopists	325/359	317/359
Chino et al[39]	Japan	Prospective observational	556	Diagnostic colonoscopy patients	AI system for polyp detection/classification	Human experts	542/556	Not reported
Djinbachian et al[40]	Canada	Prospective randomized trial	52	Screening colonoscopy patients	Autonomous AI system	Human experts	48/52	44/52 (expert 1), 40/52 (expert 2)
Kudo et al[26]	Japan	Prospective observational	194	Patients with colorectal polyps	AI with magnifying narrow band imaging (EndoBRAIN system)	Human experts	188/194	161/194
Mori et al[23]	Japan	Prospective single-center	287	Patients undergoing magnifying endoscopy	AI-assisted endocytoscopy (stained mode)	Human experts	263/287	Not reported
Renner et al[24]	Germany	Single-center observational	52	Adults with colorectal polyps	AI-assisted histology prediction	Two expert readers	48/52	44/52 (expert 1), 40/52 (expert 2)
Sato et al[27]	Netherlands	Prospective multicenter	217	Patients undergoing colonoscopy	AI using magnifying BLI	Human endoscopists	214/217	177/217
van der Zander et al[28]	Netherlands	Prospective multicenter	100	Patients with colorectal polyps	AI system with heatmapping and imaging	Two expert readers	92/100	84/100 (expert 1), 77/100 (expert 2)
Wang et al[25]	China	Prospective randomized tandem	144	Screening colonoscopy patients	Deep learning CNN (EndoScreener)	Human endoscopists	124/144	96/144

Summarizes the key characteristics of the nine included studies, detailing their design, populations, artificial intelligence interventions, comparison groups, and diagnostic outcomes. This overview provides essential context for interpreting the meta-analysis results on artificial intelligence vs human performance in colorectal polyp histology prediction. AI: Artificial intelligence; EndoBRAIN: Artificial intelligence-based system using magnifying narrow band imaging; BLI: Blue light imaging; CNN: Convolutional neural network; EndoScreener: Deep learning artificial intelligence system for colonoscopy.