ArticleJournal of gastroenterology2020
Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors.
Article in Journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05151939 (Endoscopic Ultrasound), which is not on this map. Cited by 47 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model
Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Practice Guidelines for the Diagnosis and Treatment of Subepithelial Lesion Observed in Upper Gastrointestinal Endoscopy.Journal of gastroenterology and hepatology · 2026Guideline
- Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis.Surgical endoscopy · 2023Pooled 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
- Multimodal models based on radiomics and deep learning in the classification of gastric stromal tumors and gastric leiomyomas.Surgical endoscopy · 2026Article
- CAD-SEL: A dual-modal colonoscopy dataset of subepithelial lesion.Scientific data · 2026Article
- Prediction Model of Malignant Risk of Gastrointestinal Stromal Tumor Based on Endoscopic Ultrasonography.Digestive diseases (Basel, Switzerland) · 2026Article
- Enhancing lymph node diagnosis: integrating deep learning with endoscopic ultrasonography: a retrospective study in China.Clinical endoscopy · 2025Article
- Comprehensive images and serum biomarkers for biliary atresia and other cholestasis in pediatrics.Scientific data · 2025Article
- Interpretable deep learning model diagnoses gastrointestinal stromal tumors and lesion characteristics with microprobe endoscopic ultrasonography.Scientific reports · 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
- An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.BMC gastroenterology · 2025Article
- A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification.Bioengineering (Basel, Switzerland) · 2025Article
- Management of subepithelial esophageal tumors.Innovative surgical sciences · 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
- Endoscopic Ultrasound and Gastric Sub-Epithelial Lesions: Ultrasonographic Features, Tissue Acquisition Strategies, and Therapeutic Management.Medicina (Kaunas, Lithuania) · 2024Review
- Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study.EClinicalMedicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough endoscopic ultrasound (EUS) is reported to be suitable for determining the layer from which subepithelial lesions (SELs) originate, it is difficult to distinguish gastrointestinal stromal tumor (GIST) from non-GIST using only EUS images. If artificial intelligence (AI) can be used for the diagnosis of SELs, it should provide several benefits, including objectivity, simplicity, and quickness. In this pilot study, we propose an AI diagnostic system for SELs and evaluate its efficacy.
methodsThirty sets each of EUS images with SELs ≥ 20 mm or < 20 mm were prepared for diagnosis by an EUS diagnostic system with AI (EUS-AI) and three EUS experts. The EUS-AI and EUS experts diagnosed the SELs using solely the EUS images. The concordance rates of the EUS-AI and EUS experts' diagnoses were compared with the pathological findings of the SELs.
resultsThe accuracy, sensitivity, and specificity for SELs < 20 mm were 86.3, 86.3, and 62.5%, respectively for the EUS-AI, and 73.3, 68.2, and 87.5%, respectively, for the EUS experts. In contrast, accuracy, sensitivity, and specificity for SELs ≥ 20 mm were 90.0, 91.7, and 83.3%, respectively, for the EUS-AI, and 53.3, 50.0, and 83.3%, respectively, for the EUS experts. The area under the curve for the diagnostic yield of the EUS-AI for SELs ≥ 20 mm (0.965) was significantly higher than that (0.684) of the EUS experts (P = 0.007).
conclusionEUS-AI had a good diagnostic yield for SELs ≥ 20 mm. EUS-AI has potential as a good option for the diagnosis of SELs.
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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.