Meta-Analysis
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Endosc. Aug 16, 2023; 15(8): 528-539
Published online Aug 16, 2023. doi: 10.4253/wjge.v15.i8.528
Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis
Rômulo Sérgio Araújo Gomes, Guilherme Henrique Peixoto de Oliveira, Diogo Turiani Hourneaux de Moura, Ana Paula Samy Tanaka Kotinda, Carolina Ogawa Matsubayashi, Bruno Salomão Hirsch, Matheus de Oliveira Veras, João Guilherme Ribeiro Jordão Sasso, Roberto Paolo Trasolini, Wanderley Marques Bernardo, Eduardo Guimarães Hourneaux de Moura
Rômulo Sérgio Araújo Gomes, Guilherme Henrique Peixoto de Oliveira, Diogo Turiani Hourneaux de Moura, Ana Paula Samy Tanaka Kotinda, Carolina Ogawa Matsubayashi, Bruno Salomão Hirsch, Matheus de Oliveira Veras, João Guilherme Ribeiro Jordão Sasso, Wanderley Marques Bernardo, Eduardo Guimarães Hourneaux de Moura, Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
Roberto Paolo Trasolini, Division of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
Author contributions: Gomes RSA contributed to the acquisition of data; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, Moura DTH, Bernardo WM, and de Moura EGH contributed to the analysis of data; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, Moura DTH, Bernardo WM, Trasolini RP, and de Moura EGH contributed to the interpretation of data; Gomes RSA, de Moura DTH, Trasolini RP, Bernardo WM, and de Moura EGH drafted the article; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, de Moura DTH, Trasolini RP, Bernardo WM, and de Moura EGH revised the manuscript; Trasolini RP revised the English language; and all author approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Guilherme Henrique Peixoto de Oliveira, MD, Doctor, Medical Assistant, Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Dr Enéas de Carvalho Aguiar, 225, 6o Andar, Bloco 3, Cerqueira Cesar ZIP, São Paulo 05403-010, Brazil. dr.guilhermehpoliveira@gmail.com
Received: March 16, 2023
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
ARTICLE HIGHLIGHTS
Research background

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.

Research motivation

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.

Research objectives

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.

Research methods

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.

Research results

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.

Research conclusions

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.

Research perspectives

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.