Artificial Intelligence–Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis

Frontiers in Oncology

Juan Du¹  Xinchen Luo²  Fei Kuang³  Song Su⁴  Bo Li⁴  De Luo⁴  Xiangdong Liu⁵  Mengjia Zhou⁶  Yong Tang⁷ 

[1] Department of Clinical Medicine, Southwest Medical University, Luzhou, China;Department of Gastroenterology, Zigong Third People’s Hospital, Zigong, China;Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China;Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China;Department of Hepatobiliary Surgery, Zigong Fourth People’s Hospital, Zigong, China;Department of Ultrasound, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;

关键词: artificial intelligence;    upper gastrointestinal tract;    early detection of cancer;    endoscopy;    systematic review;   

DOI  :  10.3389/fonc.2022.855175

来源: DOAJ

【 摘 要 】

ObjectiveThe aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images.MethodsDatabases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated.ResultsOverall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively.ConclusionsOn the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

【 授权许可】

Unknown