Accuracy of endoscopy-based artificial intelligence-assisted diagnostic system in the diagnosis of early esophageal cancer: A systematic review and meta-analysis
- VernacularTitle:基于内镜的人工智能辅助诊断系统在早期食管癌诊断中准确性的系统评价与Meta分析
- Author:
Ziqiang HONG
1
,
2
;
Dacheng JIN
3
;
Hongchao LI
1
,
2
;
Tao CHENG
1
,
2
;
Xiangdou BAI
1
,
2
;
Xusheng WU
1
,
2
;
Baiqiang CUI
1
,
2
;
Yunjiu GOU
3
Author Information
1. 1. The First Clinical Department of Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, P. R. China
2. 2. Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China
3. Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P. R. China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
endoscopy;
early esophageal cancer;
diagnosis;
systematic review/meta-analysis
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2023;30(09):1329-1336
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the accuracy of endoscopy-based artificial intelligence (AI)-assisted diagnostic systems in the diagnosis of early-stage esophageal cancer and provide a scientific basis for its diagnostic value. Methods PubMed, EMbase, The Cochrane Library, Web of Science, Wanfang database, VIP database and CNKI database were searched by computer to search for the relevant literature about endoscopy-based AI-assisted diagnostic systems for the diagnosis of early esophageal cancer from inception to March 2022. The QUADAS-2 was used for quality evaluation of included studies. Meta-analysis of the literature was carried out using Stata 16, Meta-Disc 1.4 and RevMan 5.4 softwares. A bivariate mixed effects regression model was utilized to calculate the combined diagnostic efficacy of the AI-assisted system and meta-regression analysis was conducted to explore the sources of heterogeneity. Results A total of 17 articles were included, which consisted of 13 retrospective cohort studies and 4 prospective cohort studies. The results of the quality evaluation using QUADAS-2 showed that all included literature was of high quality. The obtained meta-analysis results revealed that the AI-assisted system in the diagnosis of esophageal cancer presented a combined sensitivity of 0.94 (95%CI 0.91 to 0.96), a specificity of 0.85 (95%CI 0.74 to 0.92), a positive likelihood ratio of 6.28 (95%CI 3.48 to 11.33), a negative likelihood ratio of 0.07 (95%CI 0.05 to 0.11), a diagnostic odds ratio of 89 (95%CI 38 to 208) and an area under the curve of 0.96 (95%CI 0.94 to 0.98). Conclusion The AI-assisted diagnostic system has a high diagnostic value for early stage esophageal cancer. However, most of the included studies were retrospective. Therefore, further high-quality prospective studies are needed for validation.
