ArticleJournal of clinical medicine2020
Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 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
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- Performance of an Artificial Intelligence Program for Assessing the Depth of Early Gastric Cancer Using Endoscopic Images.Endoscopy international open · 2026Article
- Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.Annals of medicine · 2025Review
- Automated endoscopic image quality assessment for robust colorectal lesion detection.BMC medical imaging · 2025Article
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.Medical & biological engineering & computing · 2025Review
- Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy.Diagnostics (Basel, Switzerland) · 2024Article
- An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.Scientific reports · 2024Article
- Computer-aided diagnosis in real-time endoscopy for all stages of gastric carcinogenesis: Development and validation study.United European gastroenterology journal · 2024Article
- 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
- Artificial intelligence in theranostics of gastric cancer, a review.Medical review (2021) · 2023Review
- Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability. Several studies developed deep-learning (DL) algorithms classifying invasion depth of gastric cancers. Nevertheless, these algorithms are intended to be used after definite diagnosis of gastric cancers, which is not always feasible in various gastric neoplasms. This study aimed to establish a DL algorithm for accurately predicting submucosal invasion in endoscopic images of gastric neoplasms. Pre-trained convolutional neural network models were fine-tuned with 2899 white-light endoscopic images. The prediction models were subsequently validated with an external dataset of 206 images. In the internal test, the mean area under the curve discriminating submucosal invasion was 0.887 (95% confidence interval: 0.849-0.924) by DenseNet-161 network. In the external test, the mean area under the curve reached 0.887 (0.863-0.910). Clinical simulation showed that 6.7% of patients who underwent gastrectomy in the external test were accurately qualified by the established algorithm for potential endoscopic resection, avoiding unnecessary operation. The established DL algorithm proves useful for the prediction of submucosal invasion in endoscopic images of gastric neoplasms.
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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.