ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2022
Artificial intelligence-based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07078136 (Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs), which is not on this map. Cited by 46 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs
Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Practice Guidelines for the Diagnosis and Treatment of Subepithelial Lesion Observed in Upper Gastrointestinal Endoscopy.Journal of gastroenterology and hepatology · 2026Guideline
- Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.Frontiers in medicine · 2025Pooled it
- Application of artificial intelligence in the diagnosis of subepithelial lesions using endoscopic ultrasonography: a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- CAD-SEL: A dual-modal colonoscopy dataset of subepithelial lesion.Scientific data · 2026Article
- A novel risk-scoring system based on endoscopic ultrasound and clinical characteristics for the preoperative diagnosis of small gastric gastrointestinal stromal tumors.Scientific reports · 2026Article
- Uncovering the associative mediation pathway from EUS features to malignancy through pathological mediators in gastrointestinal stromal tumor.Frontiers in medicine · 2026Article
- Artificial intelligence-based endoscopic ultrasonography model for detecting the origin layer of gastric subepithelial lesions.Frontiers in medicine · 2026Article
- Development and validation of an interpretable predictive model for mismatch between endoscopic ultrasonography and histopathological diagnosis in colorectal subepithelial lesions: a retrospective single-center study.Frontiers in medicine · 2026Article
- Interpretable deep learning model diagnoses gastrointestinal stromal tumors and lesion characteristics with microprobe endoscopic ultrasonography.Scientific reports · 2025Article
- Proton radiography interpretation with artificial intelligence for treatment deviation detection in proton therapy.Physics and imaging in radiation oncology · 2025Article
- Practice Guidelines for the Diagnosis and Treatment of Subepithelial Lesions Observed in Upper Gastrointestinal Endoscopy.The Korean journal of helicobacter and upper gastrointestinal research · 2025Review
- Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video).BMC medical informatics and decision making · 2025Article
- Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features.BMC gastroenterology · 2025Article
- Artificial intelligence-assisted endoscopic ultrasound diagnosis of esophageal subepithelial lesions.Surgical endoscopy · 2025Article
- A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification.Bioengineering (Basel, Switzerland) · 2025Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.Frontiers in genetics · 2025Review
- Correlation of preoperative CT features with intra- and postoperative parameters of endoscopic resection in patients with gastric submucosal tumor (1~3 cm).Surgical endoscopy · 2025Article
- Review
- Understanding Gastric GIST: From Pathophysiology to Personalized Treatment.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
26 authors.
Funding
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Abstract
backgroundEndoscopic ultrasonography (EUS) is useful for the differential diagnosis of subepithelial lesions (SELs); however, not all of them are easy to distinguish. Gastrointestinal stromal tumors (GISTs) are the commonest SELs, are considered potentially malignant, and differentiating them from benign SELs is important. Artificial intelligence (AI) using deep learning has developed remarkably in the medical field. This study aimed to investigate the efficacy of an AI system for classifying SELs on EUS images.
methodsEUS images of pathologically confirmed upper gastrointestinal SELs (GIST, leiomyoma, schwannoma, neuroendocrine tumor [NET], and ectopic pancreas) were collected from 12 hospitals. These images were divided into development and test datasets in the ratio of 4:1 using random sampling; the development dataset was divided into training and validation datasets. The same test dataset was diagnosed by two experts and two non-experts.
resultsA total of 16,110 images were collected from 631 cases for the development and test datasets. The accuracy of the AI system for the five-category classification (GIST, leiomyoma, schwannoma, NET, and ectopic pancreas) was 86.1%, which was significantly higher than that of all endoscopists. The sensitivity, specificity, and accuracy of the AI system for differentiating GISTs from non-GISTs were 98.8%, 67.6%, and 89.3%, respectively. Its sensitivity and accuracy were significantly higher than those of all the endoscopists.
conclusionThe AI system, classifying SELs, showed higher diagnostic performance than that of the experts and may assist in improving the diagnosis of SELs in clinical practice.
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