ReviewCurrent treatment options in gastroenterology2020
Artificial Intelligence and Polyp Detection.
Review in Current treatment options in gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial.BMJ open gastroenterology · 2024Trial
- A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.Scientific reports · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.PloS one · 2024Article
- Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review.Journal of cancer research and clinical oncology · 2023Review
- Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.Journal of the Canadian Association of Gastroenterology · 2023Article
- Automatic Feature Segmentation in Dental Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
- Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists.Biomedical engineering online · 2022Article
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm.Gut and liver · 2021Review
- [Robots in the operating room-(co)operation during surgery].Der Gastroenterologe : Zeitschrift fur Gastroenterologie und Hepatologie · 2021Review
- Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.Clinical and experimental gastroenterology · 2021Article
- Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials.EClinicalMedicine · 2020Article
- Artificial intelligence technologies for the detection of colorectal lesions: The future is now.World journal of gastroenterology · 2020Review
- Artificial intelligence in gastrointestinal endoscopy: current evidence and future directions.Therapeutic advances in gastrointestinal endoscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purpose of reviewThis review highlights the history, recent advances, and ongoing challenges of artificial intelligence (AI) technology in colonic polyp detection. RECENT
findingsHand-crafted AI algorithms have recently given way to convolutional neural networks with the ability to detect polyps in real-time. The first randomized controlled trial comparing an AI system to standard colonoscopy found a 9% increase in adenoma detection rate, but the improvement was restricted to polyps smaller than 10 mm and the results need validation. As this field rapidly evolves, important issues to consider include standardization of outcomes, dataset availability, real-world applications, and regulatory approval. SUMMARY: AI has shown great potential for improving colonic polyp detection while requiring minimal training for endoscopists. The question of when AI will enter endoscopic practice depends on whether the technology can be integrated into existing hardware and an assessment of its added value for patient care.
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.