SynthesisBMC gastroenterology2022
Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps.
Synthesis in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review.Frontiers in oncology · 2025Pooled it
- Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Deep learning model for predicting lymph node metastasis around rectal cancer based on rectal tumor core area and mesangial imaging features.BMC medical imaging · 2025Article
- Advances in gastrointestinal endoscopy: A comprehensive review of innovations in cancer diagnosis and management.World journal of gastrointestinal endoscopy · 2025Review
- [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor.Diagnostics (Basel, Switzerland) · 2025Article
- Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system.Frontiers in oncology · 2024Article
- The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization.Cancers · 2023Review
- Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods.Journal of imaging · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveThe main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience.
methodsWe searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3.
resultsA total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05).
conclusionThe performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.