Artificial intelligence - upping the game in gastrointestinal endoscopy?

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

12 citing papers in PubMed.

1. Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial.Trials · 2024
   Article
2. Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach.Bioengineering (Basel, Switzerland) · 2023
   Article
3. Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.JMIR cancer · 2021
   Review
4. Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis.Annals of translational medicine · 2021
   Article
5. Transcription factors linked to the molecular signatures in the development of hepatocellular carcinoma on a cirrhotic background.Medical oncology (Northwood, London, England) · 2021
   Article
6. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions.Gastroenterology report · 2021
   Review
7. Challenges in Crohn's Disease Management after Gastrointestinal Cancer Diagnosis.Cancers · 2021
   Review
8. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.Aging and disease · 2020
   Review
9. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects.World journal of urology · 2020
   Review
10. Use of Artificial Intelligence-Based Analytics From Live Colonoscopies to Optimize the Quality of the Colonoscopy Examination in Real Time: Proof of Concept.Gastroenterology · 2020
    Article
11. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov.Frontiers in oncology · 2020
    Article
12. A pilot evaluation of the artificial intelligence system CAD-EYE to optically characterise lesions in inflammatory bowel disease surveillance.Therapeutic advances in gastrointestinal endoscopy
    Article

Corrections and comments

• Comment on
  Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.2019
• Comment on
  Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy.2019

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as