ReviewBiomedical engineering online2022
Deep learning for gastroscopic images: computer-aided techniques for clinicians.
Review in Biomedical engineering online, 2022. 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
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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.
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Who cites it
15 citing papers in PubMed, 34 citations in OpenAlex.
- Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.Surgical endoscopy · 2026Article
- FDSS-Net: feature enhancement and dual-stream semantic mixture network for polyp segmentation.Scientific reports · 2026Article
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
- Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study.World journal of gastroenterology · 2025Article
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy.Scientific reports · 2025Article
- Classical biomarkers and non-coding RNAs associated with diagnosis and treatment in gastric cancer.Oncology research · 2025Review
- Improving image-retrieval performance of foundation models in gastrointestinal endoscopic images.Frontiers in medicine · 2025Article
- Bidirectional dynamic frame prediction network for total-body [EJNMMI physics · 2024Article
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer.Journal of robotic surgery · 2024Review
- Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images.Scientific reports · 2024Article
- Exploring factors that influence the behavioural intention of medical students to use 3D gastroscopic model to learn how to operate gastroscope using UTAUT Model.BMC medical education · 2023Article
- Detection and Characterization of Gastric Cancer Using Cascade Deep Learning Model in Endoscopic Images.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
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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.