期刊
INTERNATIONAL JOURNAL OF COLORECTAL DISEASE
卷 37, 期 3, 页码 495-506出版社
SPRINGER
DOI: 10.1007/s00384-021-04062-x
关键词
Artificial intelligence; Colonoscopy; Adenoma detection; Polyp detection; Meta-analysis
This meta-analysis included 10 randomized controlled trials with 6629 individuals in AI-aided and routine groups, showing that AI-aided endoscopy significantly improved ADR and PDR compared to routine colonoscopy. Lesion detection rates such as APC, PPC, and SSLPC were significantly higher in the AI-aided group, while withdrawal time was longer in the AI-aided group when biopsies were included.
Background This meta-analysis aimed to determine whether artificial intelligence (AI) improves colonoscopy outcome metrics i.e. adenoma detection rate (ADR) and polyp detection rate (PDR). Methods Two authors independently searched Web of Science, PubMed, Science Direct, and Cochrane Library to find all published research before July 2021 that has compared AI-aided colonoscopy with routine colonoscopy (RC) for detection of adenoma and polyp. Results This meta-analysis included 10 RCTs with 6629 individuals in AI-aided (n = 3300) and routine (n = 3329) groups. The results showed that both ADR (RR, 1.43; P < 0.001) and PDR (RR, 1.44; P < 0.001) using AI-aided endoscopy were significantly greater when compared with RC. The adenomas detected per colonoscopy (APC) (WMD, 0.25; P = 0.009), polyps detected per colonoscopy (PPC) (WMD, 0.52; P < 0.001), and sessile serrated lesions detected per colonoscopy (SSLPC) (RR, 1.53; P < 0.001) were significantly higher in the AI-aided group compared with the RC group. Subgroup analysis based on size, location, and shape of adenomas and polyps demonstrated that, except for in the cecum and pedunculated adenomas or polyps, the AI-aided groups of the other subgroups are more advantageous. Withdrawal time was longer in the AI-aided group when biopsies were included, while withdrawal time excluding biopsy time showed no significant difference. Conclusions AI-aided polyp detection system significantly increases lesion detection rate. In addition, lesion detection by AI is hardly affected by factors such as size, location, and shape.
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