ReviewWorld journal of gastroenterology2020
Artificial intelligence technologies for the detection of colorectal lesions: The future is now.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 31 citations in OpenAlex.
- Beyond diagnostic accuracy: navigating the artificial intelligence data tsunami toward value-based implementation in gastroenterology.Frontiers in artificial intelligence · 2026Review
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
- The adult large bowel: describing environment morphology for effective biomedical device development.Progress in biomedical engineering (Bristol, England) · 2024Review
- Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions.Frontiers in oncology · 2024Review
- The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof.Annals of coloproctology · 2023Review
- Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy.Surgical endoscopy · 2023Article
- Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.Surgical endoscopy · 2022Article
- Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.World journal of gastroenterology · 2022Review
- A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.Scientific reports · 2022Article
- The role of artificial intelligence based systems for cost optimization in colorectal cancer prevention programs.Frontiers in artificial intelligence · 2022Review
- Artificial intelligence applications in gastroenterology: steps ahead.Medicine and pharmacy reports · 2021Article
- Usefulness of artificial intelligence in gastric neoplasms.World journal of gastroenterology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in
Indexed as
Identifiers
What OpenQuestion holds
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.