ArticleClinical and experimental gastroenterology2021
Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.
Article in Clinical and experimental gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Development and validation of a real-time AI model for differentiating benign and malignant gastric ulcers : a multicenter retrospective study.BMC gastroenterology · 2026Article
- Multi-region and multi-image convolutional neural network model for detecting gastric helicobacter pylori infection.Surgical endoscopy · 2026Article
- The index endoscopic characteristics associated with gastric neoplasms in serial screening of upper gastrointestinal endoscopy.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Article
- Immunoglobulin G4 -related gastrointestinal disease associated with type 1 autoimmune pancreatitis: A case report.DEN open · 2025Article
- Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.BMC medical informatics and decision making · 2025Article
- A fusion model of manually extracted visual features and deep learning features for rebleeding risk stratification in peptic ulcers.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- [Research progress on endoscopic image diagnosis of gastric tumors based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.Health data science · 2023Review
- Innovation in Gastroenterology-Can We Do Better?Biomimetics (Basel, Switzerland) · 2022Article
- Application Status and Prospects of Artificial Intelligence in Peptic Ulcers.Frontiers in surgery · 2022Review
- Automated Disease Detection in Gastroscopy Videos Using Convolutional Neural Networks.Frontiers in medicine · 2022Article
- Key research questions for implementation of artificial intelligence in capsule endoscopy.Therapeutic advances in gastroenterology · 2022Article
- Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature.Journal of clinical medicine · 2021Review
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5 authors.
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Abstract
BACKGROUND AND
aimEndoscopic differentiation between malignant and benign gastric ulcers (GU) affects further evaluation and prognosis. The aim of our study was to evaluate a deep learning algorithm for discrimination between benign and malignant GU in a database of endoscopic ulcer images.
methodsWe retrospectively collected consecutive upper gastrointestinal endoscopy images of GU performed between 2011 and 2019 at the Sheba Medical Center. All ulcers had a corresponding histopathology result of either benign peptic ulcer or gastric adenocarcinoma. A convolutional neural network (CNN) was trained to classify the images into either benign or malignant. Endoscopies from 2011 to 2017 were used for training (2011-2015) and validation (2016-2017). Hyper-parameters, image augmentation and pre-training on Google images obtained images were evaluated on the validation data. Held-out data from 2018 to 2019 was used for testing the final model.
resultsOverall, the Sheba dataset included 1978 GU images; 1894 images from benign GU and 84 images of malignant ulcers. The final CNN model showed an AUC 0.91 (95% CI 0.85-0.96) for detecting malignant ulcers. For cut-off probability 0.5, the network showed a sensitivity of 92% and specificity of 75% for malignant ulcers.
conclusionOur study displays the applicability of a CNN model for automated evaluation of gastric ulcers images for malignant potential. Following further research, the algorithm may improve accuracy of differentiating benign from malignant ulcers during endoscopies and assist in patients' stratification, allowing accelerated patients management and individualized approach towards surveillance endoscopy.
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