ArticleClinical endoscopy2022
Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach.
Article in Clinical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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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, 2 syntheses or guidelines pooled it.
- Pooled it
- Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Trial
- Gastric mucosa intestinal metaplasia segmentation and grading via transformer-CNN fusion architecture: an interpretable digital pathology diagnostic framework.Medical & biological engineering & computing · 2026Article
- Boundary-guided VMUNet for atrophic gastritis lesion segmentation in magnifying endoscopy with narrow-band imaging.Frontiers in medicine · 2026Article
- An interpretable deep learning framework for intestinal metaplasia detection in gastric histopathology images.Frontiers in oncology · 2026Article
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- ViSwNeXtNet Deep Patch-Wise Ensemble of Vision Transformers and ConvNeXt for Robust Binary Histopathology Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Computer-aided diagnosis in real-time endoscopy for all stages of gastric carcinogenesis: Development and validation study.United European gastroenterology journal · 2024Article
- As how artificial intelligence is revolutionizing endoscopy.Clinical endoscopy · 2024Review
- Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024Review
- Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND/
aimsPrevious artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy.
methodsInvestigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIM areas in real time. The results were analyzed using different validity values.
resultsFrom the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%, 80%, 82%, 92%, 87%, and 57%, respectively.
conclusionThe bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization, and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.
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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.