SynthesisSurgical endoscopy2023
Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis.
Synthesis in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 37 citations in OpenAlex.
- Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.Diagnostics (Basel, Switzerland) · 2026Review
- Endoscopic resection for gastric subepithelial lesions: overtreatment or the next step in optimization?Clinical endoscopy · 2026Article
- Comparative Evaluation of Endoscopic Ultrasonography and Multi-Slice Spiral CT in Diagnosing Gastrointestinal Stromal Tumors: A Retrospective Study.International journal of general medicine · 2026Article
- The Mechanisms of Imatinib Resistance in Gastrointestinal Stromal Tumours: Theoretical Basis and Therapeutic Aspect.Journal of cellular and molecular medicine · 2025Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Interpretable deep learning model diagnoses gastrointestinal stromal tumors and lesion characteristics with microprobe endoscopic ultrasonography.Scientific reports · 2025Article
- Deep Learning Algorithm to Determine the Presence of Rectal Cancer from Transrectal Ultrasound Images.Life (Basel, Switzerland) · 2025Article
- 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
- Diagnostic accuracy and influencing factors of microprobe endoscopic ultrasound for gastrointestinal subepithelial lesions: a multicenter retrospective study.BMC gastroenterology · 2025Article
- A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.DEN open · 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
- Case Report: Dramatic response to entritinib in a patient with gastrointestinal stromal tumor positive forFrontiers in oncology · 2025Article
- Endoscopic Ultrasound and Gastric Sub-Epithelial Lesions: Ultrasonographic Features, Tissue Acquisition Strategies, and Therapeutic Management.Medicina (Kaunas, Lithuania) · 2024Review
- Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid.Sensors (Basel, Switzerland) · 2023Article
- Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsEndoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images.
methodsPubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy.
resultsOverall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89-0.95), 0.82 (95% CI 0.75-0.87), 4.55 (95% CI 2.64-7.84), and 0.12 (95% CI 0.07-0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83-175.69) and 0.950 (Q* = 0.891).
conclusionsAI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future.
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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.