Binglan Zhang

  • Designation: Lecturer, Attending Physician, Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University
  • Country: China
  • Title: Artificial Intelligence‑Assisted Endoscopic Ultrasound in the Diagnosis of Gastrointestinal Stromal Tumors: A Meta‑Analysis


Zhang Binglan, female, Ph.D attending physician, lecturer. She is working in the department of gastroenterology, the first affiliated hospital of chongqing medical university, Chongqing, China. She is currently the deputy leader of the early cancer group of Chongqing Digestive Endoscopy Special Committee, a member of Chongqing Digestive Youth Committee, and a member of the early cancer group of Chongqing Anti-Cancer Association. Her main research direction is endoscopic ultrasound, the biological treatment of digestive tract tumors and the related research of CAR-T cell therapy for digestive tract tumors.


Endoscopic 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. PubMed, 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. Overall, 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). AI-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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