Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Aling Wang; Jiahao Mo; Cailing Zhong; Shaohua Wu; Sufen Wei; Binqi Tu; Chang Liu; Daman Chen; Qing Xu; Mengyi Cai; Zhuoyao Li; Wenting Xie; Miao Xie; Motohiko Kato; Xujie Xi; Beiping Zhang

doi:10.21037/atm-21-5081

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Ann Transl Med. 2021 Nov;9(22):1662. doi: 10.21037/atm-21-5081.

Authors

Aling Wang^#¹, Jiahao Mo^#¹, Cailing Zhong^#², Shaohua Wu¹, Sufen Wei², Binqi Tu¹, Chang Liu¹, Daman Chen³, Qing Xu³, Mengyi Cai³, Zhuoyao Li³, Wenting Xie³, Miao Xie³, Motohiko Kato⁴, Xujie Xi², Beiping Zhang²

Affiliations

¹ The Second Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China.
² Department of Gastroenterology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
³ Guangzhou University of Chinese Medicine, Guangzhou, China.
⁴ Division of Gastroenterology and Hepatology, Department of Internal Medicine Keio University School of Medicine, Tokyo, Japan.

^# Contributed equally.

Abstract

Background: Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy.

Methods: The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.

Results: Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89-0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79-0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88-0.95) with a specificity of 0.82 (95% CI: 0.71-0.89) and an AUC of 0.94 (95% CI: 0.92-0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94-0.97), a specificity of 0.88 (95% CI: 0.74-0.95), and an AUC of 0.97 (95% CI: 0.95-0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92-0.96) with a specificity of 0.95 (95% CI: 0.80-0.99) and an AUC of 0.97 (95% CI: 0.95-0.98).

Discussion: The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.

Keywords: Artificial intelligence (AI); colonoscopy; colorectal polyps; meta-analysis.