ArticleSurgical endoscopy2022
Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy.
Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Operation sequence of bidirectional endoscopy with topical anesthesia affected colonoscopy performance: a randomized controlled trial.BMC gastroenterology · 2025Trial
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- A Video-Based Artificial Intelligence Platform for Measuring Esophagogastroduodenoscopy Duration and Suggesting Real-Time Landmark Documentation.Endoscopy international open · 2026Article
- Artificial Intelligence in Upper Gastrointestinal Endoscopy: Current Evidence, Practice, and Future Directions.Cureus · 2025Review
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
- Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025Article
- Success Rate and Factors Associated With Surveillance Endoscopy After Proximal Gastrectomy With Double-Tract Reconstruction: A Retrospective Multicenter Cohort Study.Journal of gastric cancer · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy.Scientific reports · 2025Article
- A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.Medical & biological engineering & computing · 2025Review
- GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.Scientific data · 2025Article
- Improving image-retrieval performance of foundation models in gastrointestinal endoscopic images.Frontiers in medicine · 2025Article
- Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024Review
- Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images.International journal of general medicine · 2024Article
- Endoscopic Imaging for the Diagnosis of Neoplastic and Pre-Neoplastic Conditions of the Stomach.Cancers · 2023Review
- An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer.Cancers · 2022Article
- Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.Surgical endoscopy · 2022Article
- Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice.Diagnostics (Basel, Switzerland) · 2022Review
- Quality indicators in esophagogastroduodenoscopy.Clinical endoscopy · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophagogastroduodenoscopy (EGD) is generally a safe procedure, but adverse events often occur. This highlights the necessity of the quality control of EGD. Complete visualization and photo documentation of upper gastrointestinal (UGI) tracts are important measures in quality control of EGD. To evaluate these measures in large scale, we developed an AI-driven quality control system for EGD through convolutional neural networks (CNNs) using archived endoscopic images.
methodsWe retrospectively collected and labeled images from 250 EGD procedures, a total of 2599 images from eight locations of the UGI tract, using the European Society of Gastrointestinal Endoscopy (ESGE) photo documentation methods. The label confirmed by five experts was considered the gold standard. We developed a CNN model for multi-class classification of EGD images to one of the eight locations and binary classification of each EGD procedure based on its completeness.
resultsOur CNN model successfully classified the EGD images into one of the eight regions of UGI tracts with 97.58% accuracy, 97.42% sensitivity, 99.66% specificity, 97.50% positive predictive value (PPV), and 99.66% negative predictive value (NPV). Our model classified the completeness of EGD with 89.20% accuracy, 89.20% sensitivity, 100.00% specificity, 100.00% PPV, and 64.94% NPV. We analyzed the credibility of our model using a probability heatmap.
conclusionsWe constructed a CNN model that could be used in the quality control of photo documentation in EGD. Our model needs further validation with a large dataset, and we expect our model to help both endoscopists and patients by improving the quality of EGD procedures.
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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.