ArticleClinical endoscopy2020
Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy.
Article in Clinical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- GastroMalign: Vision Transformer-Based Framework for Early Detection and Malignancy-Risk Stratification for High-Risk Gastrointestinal Lesions.Journal of clinical medicine · 2026Article
- Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study.World journal of gastroenterology · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Usefulness of an artificial intelligence-based colonoscopy report generation support system.Clinical endoscopy · 2025Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection.Scientific reports · 2024Article
- Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images.International journal of general medicine · 2024Article
- Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives.BMJ open · 2023Article
- The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof.Annals of coloproctology · 2023Review
- Deep learning to predict esophageal variceal bleeding based on endoscopic images.The Journal of international medical research · 2023Article
- Validation of AI-based software for objectification of conjunctival provocation test.The journal of allergy and clinical immunology. Global · 2023Article
- Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data.Journal of digital imaging · 2023Article
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Advanced Development and New Horizons.Gastroenterology research and practice · 2023Review
- Review
- Recognition of esophagitis in endoscopic images using transfer learning.World journal of gastrointestinal endoscopy · 2022Article
- Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future.Journal of clinical medicine · 2022Review
- Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.Scientific reports · 2022Article
- Efficient Synchronous Real-Time CADe for Multicategory Lesions in Gastroscopy by Using Multiclass Detection Model.BioMed research international · 2022Article
- Artificial intelligence applications in gastroenterology: steps ahead.Medicine and pharmacy reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.
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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.