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Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis:A systematic review and meta-analysis

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World Journal of Gastrointestinal Endoscopy 2023年第8期15卷 528-539页

作者： 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 Department of Gastroenterology Hospital das Clínicas da Faculdade de Medicina da Universidade de São PauloSão Paulo 05403-010Brazil Division of Hepatology and Endoscopy Department of GastroenterologyHarvard Medical SchoolBrigham and Women’s HospitalBostonMA 02115United States

BACKGROUND Subepithelial lesions(SELs)are gastrointestinal tumors with heterogeneous malignant *** ultrasonography(EUS)is the leading method for evaluation,but without histopathological analysis,precise differentiation of SEL risk is *** intelligence(AI)is a promising aid for the diagnosis of gastrointestinal lesions in the absence of *** To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs,especially lesions originating from the muscularis propria *** Electronic databases including PubMed,EMBASE,and Cochrane Library were *** of any sex and>18 years,with SELs assessed by EUS AIassisted,with previous histopathological diagnosis,and presented sufficient data values which were extracted to construct a 2×2 *** reference standard was *** primary outcome was the accuracy of AI for gastrointestinal stromal tumor(GIST).Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma(GIL),the diagnostic performance of experienced endoscopists for GIST,and GIST vs *** sensitivity,specificity,positive,and negative predictive values were *** corresponding summary receiver operating characteristic curve and post-test probability were also *** Eight retrospective studies with a total of 2355 patients and 44154 images were included in this *** 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 area under the curve(AUC)of *** diagnosis of GIST vs GIL by AI-assisted EUS,specificity was 90%(95%CI:0.88-0.95;P=0.02)and AUC of *** experienced endoscopists’values were 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 AUC of 0.777 for *** GIST vs GIL,the experts achieved a sensitivity of 73%(95%CI:0.65-0.80;P<0.01)and an AUC of ***

关键词： Subepithelial lesions Ultrasound endoscopy Artificial intelligence

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