Diagnostic value of deep learning-assisted endoscopic ultrasound for pancreatic tumors: a systematic review and meta-analysis

Frontiers in Oncology

Oncology

Kunhong Wang¹  Ning Wei¹  Tao Tao¹  Yanting Shi¹  Feng Yu¹  Bing Lv² 

[1] Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China;School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China;

关键词: pancreatic tumor;    artificial intelligence;    deep learning;    endoscopic ultrasound;    meta-analysis;    systemic review;   

DOI  :  10.3389/fonc.2023.1191008

 received in 2023-03-21, accepted in 2023-07-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background and aimsEndoscopic ultrasonography (EUS) is commonly utilized in the diagnosis of pancreatic tumors, although as this modality relies primarily on the practitioner’s visual judgment, it is prone to result in a missed diagnosis or misdiagnosis due to inexperience, fatigue, or distraction. Deep learning (DL) techniques, which can be used to automatically extract detailed imaging features from images, have been increasingly beneficial in the field of medical image-based assisted diagnosis. The present systematic review included a meta-analysis aimed at evaluating the accuracy of DL-assisted EUS for the diagnosis of pancreatic tumors diagnosis.MethodsWe performed a comprehensive search for all studies relevant to EUS and DL in the following four databases, from their inception through February 2023: PubMed, Embase, Web of Science, and the Cochrane Library. Target studies were strictly screened based on specific inclusion and exclusion criteria, after which we performed a meta-analysis using Stata 16.0 to assess the diagnostic ability of DL and compare it with that of EUS practitioners. Any sources of heterogeneity were explored using subgroup and meta-regression analyses.ResultsA total of 10 studies, involving 3,529 patients and 34,773 training images, were included in the present meta-analysis. The pooled sensitivity was 93% (95% confidence interval [CI], 87–96%), the pooled specificity was 95% (95% CI, 89–98%), and the area under the summary receiver operating characteristic curve (AUC) was 0.98 (95% CI, 0.96–0.99).ConclusionDL-assisted EUS has a high accuracy and clinical applicability for diagnosing pancreatic tumors.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023391853, identifier CRD42023391853.

【 授权许可】

Unknown   
Copyright © 2023 Lv, Wang, Wei, Yu, Tao and Shi