Published online Aug 16, 2023. doi: 10.4253/wjge.v15.i8.528
Peer-review started: March 16, 2023
First decision: April 20, 2023
Revised: June 15, 2023
Accepted: July 24, 2023
Article in press: July 24, 2023
Published online: August 16, 2023
Processing time: 142 Days and 22.4 Hours
Endoscopic ultrasonography (EUS) with artificial intelligence (AI) has shown high diagnostic accuracy for subepithelial lesions (SELs), particularly gastrointestinal stromal tumors (GISTs). The performance of AI systems has demonstrated superiority over experienced endoscopists and the ability to improve diagnostic power through collaborative diagnosis.
This paper aims to investigate the diagnostic capabilities of AI-assisted EUS for SELs by analyzing images and comparing them with the expertise of experienced endoscopists.
The research aims to assess the accuracy of AI-assisted EUS in diagnosing SELs, particularly those originating from the fourth layer. Additionally, the study analyzes the diagnostic performance of experienced endoscopists and compares it with AI systems.
Retrospective studies were selected of AI-assisted EUS for the diagnosis of SELs, using histopathology as the standard method. The included studies utilized EUS with AI for SELs diagnosis through image analysis. The risk of bias and quality of evidence were assessed, and the analysis was performed using Meta-Disc software.
This meta-analysis included eight retrospective studies with a total of 2355 patients and 44154 images. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01], specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and an AUC of 0.949. For the diagnosis of GIST vs gastrointestinal leiomyoma (GIL) by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC 0.966. The experienced endoscopists achieved a sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and an AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819.
This systematic review and meta-analysis demonstrate the high diagnostic accuracy of AI-assisted EUS in differentiating SELs, particularly GIST, from other fourth-layer subepithelial tumors.
This study demonstrated that by integrating machine learning techniques with EUS images, AI can aid in distinguishing benign from malignant lesions and guiding treatment decisions, with high accuracy. Additionally, through AI assistance image recognition can enhance real-time diagnosis during EUS evaluations, increasing the performance of even experienced endoscopists.