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

De Luo; Fei Kuang; Juan Du; Mengjia Zhou; Xiangdong Liu; Xinchen Luo; Yong Tang; Bo Li; Song Su

doi:10.3389/fonc.2022.855175

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

Front Oncol. 2022 Jun 10:12:855175. doi: 10.3389/fonc.2022.855175. eCollection 2022.

Authors

De Luo¹, Fei Kuang², Juan Du³, Mengjia Zhou⁴, Xiangdong Liu⁵, Xinchen Luo⁶, Yong Tang⁷, Bo Li¹, Song Su¹

Affiliations

¹ Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
² Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China.
³ Department of Clinical Medicine, Southwest Medical University, Luzhou, China.
⁴ Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁵ Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China.
⁶ Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China.
⁷ School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

Objective: The 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.

Methods: Databases 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.

Results: Overall, 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.

Conclusions: On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

Keywords: artificial intelligence; early detection of cancer; endoscopy; systematic review; upper gastrointestinal tract.

Publication types

Systematic Review