ArticleJGH open : an open access journal of gastroenterology and hepatology2020
Stratification of gastric cancer risk using a deep neural network.
Article in JGH open : an open access journal of gastroenterology and hepatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis.Frontiers in oncology · 2025Pooled it
- Recent advancement in endoscopic diagnosis for risk stratification of gastric cancer.Clinical endoscopy · 2025Review
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology.Diagnostics (Basel, Switzerland) · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial.Endoscopy international open · 2021Article
- Advancing care for acute gastrointestinal bleeding using artificial intelligence.Journal of gastroenterology and hepatology · 2021Article
- Artificial intelligence in gastric cancer: Application and future perspectives.World journal of gastroenterology · 2020Review
- Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment.Frontiers in molecular biosciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimStratifying gastric cancer (GC) risk and endoscopy findings in high-risk individuals may provide effective surveillance for GC. We developed a computerized image- analysis system for endoscopic images to stratify the risk of GC.
methodsThe system was trained using images taken during endoscopic examinations with non-magnified white-light imaging. Patients were classified as high-risk (patients with GC), moderate-risk (patients with current or past
resultsPerformance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and
conclusionThe current AI system detected significant differences in the prevalence of GC among the low-, moderate-, and high-risk groups, suggesting its potential for stratifying GC risk.
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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.