Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis

Ishita Barua; Daniela Guerrero Vinsard; Henriette C. Jodal; Magnus Løberg; Mette Kalager; Øyvind Holme; Masashi Misawa; Michael Bretthauer; Yuichi Mori

doi:10.1055/a-1201-7165

Endoscopy, Table of Contents

Endoscopy 2021; 53(03): 277-284
DOI: 10.1055/a-1201-7165

Systematic review

Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis

Ishita Barua^*

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Daniela Guerrero Vinsard^*

²Department of Internal Medicine, University of Connecticut Health Centre, Connecticut, USA

³Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA

,

Henriette C. Jodal

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Magnus Løberg

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Mette Kalager

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Øyvind Holme

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Masashi Misawa

⁴Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

,

Michael Bretthauer

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

,

Yuichi Mori

¹Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway

⁴Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

› Author Affiliations

Abstract

Background Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality.

Patients and methods: We performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer.

Results Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % – 37.0 %) versus 19.3 % (12.7 % – 25.9 %) without AI; relative risk (RR] 1.52 (1.31 – 1.77), with high certainty. PDR was 45.4 % (41.1 % – 49.8 %) with AI versus 30.6 % (26.5 % – 34.6 %) without AI; RR 1.48 (1.37 – 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 [0.12 – 0.18]), but not for larger adenomas (> 5 – ≤ 10 mm, mean difference 0.03 [0.01 – 0.05]; > 10 mm, mean difference 0.01 [0.00 – 0.02]; high certainty). Data on cancer are unavailable.

Conclusions AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas.

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Supplementary Material

Supplementary material