ArticleNPJ digital medicine2022
An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Diagnostic Applications of Artificial Intelligence in Liver Diseases.Journal of clinical medicine · 2025Review
- Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy.World journal of gastrointestinal endoscopy · 2025Review
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Visualizing global progress and challenges in esophagogastric variceal bleeding.World journal of gastrointestinal surgery · 2025Article
- Endoscopic ultrasound-guided therapies in the treatment of gastric varices: An in-depth examination of associated adverse events.World journal of gastrointestinal endoscopy · 2024Article
- Esophageal varices detection and bleeding risk assessment with artificial intelligence: a systematic review.iGIE : innovation, investigation and insights · 2024Article
- Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature.BMC medical imaging · 2024Article
- The Prediction and Treatment of Bleeding Esophageal Varices in the Artificial Intelligence Era: A Review.Cureus · 2024Review
- Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024Review
- ENDOANGELTherapeutic advances in gastroenterology · 2023Article
- Challenges and Future Perspectives for Artificial Intelligence in Hepatology: Breaking Barriers for Better Care.Journal of clinical and experimental hepatologyReview
- Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study.Science progressArticle
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1-3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p < 0.001). When ranking the risk of patients combined with the Child‒Pugh score, ENDOANGEL-GEV outperforms endoscopists for esophageal varices (p < 0.001) and shows comparable performance for gastric varices (p = 0.152). Compared with endoscopists, ENDOANGEL-GEV may help 12.31% (16/130) more patients receive the right intervention. We establish an interpretable system for the endoscopic diagnosis and risk stratification of GEV. It will assist in detecting the first bleeding risk factors accurately and expanding the scope of quantitative measurement of diseases.
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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.