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, Inhaltsverzeichnis

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

› Institutsangaben

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.

Volltext

Referenzen

References
1 Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7-34
2 Zauber AG, Winawer SJ, O’Brien MJ. et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med 2012; 366: 686-696
3 Lauby-Secretan B, Vilahur N, Bianchini F. et al. for the International Agency for Research on Cancer Handbook Working Group The IARC perspective on colorectal cancer screening. N Engl J Med 2018; 378: 1734-1740
4 Kaminski MF, Regula J, Kraszewska E. et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010; 362: 1795-1803
5 Corley DA, Jensen CD, Marks AR. et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 1298-1306
6 Berzin TM, Topol EJ. Adding artificial intelligence to gastrointestinal endoscopy. Lancet 2020; 395: 485
7 Vinsard DG, Mori Y, Misawa M. et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc 2019; 90: 55-63
8 Kalager M, Wieszczy P, Lansdorp-Vogelaar I. et al. Overdiagnosis in colorectal cancer screening: time to acknowledge a blind spot. Gastroenterology 2018; 155: 592-595
9 Guyatt GH, Oxman AD, Vist GE. et al. GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. Chinese J Evidence-Based Med 2009; 9: 8-11
10 Akl EA, Sun X, Busse JW. et al. Specific instructions for estimating unclearly reported blinding status in randomized trials were reliable and valid. J Clin Epidemiol 2012; 65: 262-267
11 Sterne JAC, Sutton AJ, Ioannidis JPA. et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 2011; 343
12 Moher D, Liberati A, Tetzlaff J. et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Int J Surg 2010; 8: 336-341
13 Cochrane Central Register of Controlled Trials. Impact of automatic polyp detection system on adenoma detection rate. https://www.cochranelibrary.com/central/doi/10.1002/central/CN-01933143/full
14 Cochrane Central Register of Controlled Trials. The effect of the colonoscopic real-time detection system of colorectal polyps within system monitor only on improving the detection rate of colorectal polyps and the influence on the fatigue of endoscopists. https://www.cochranelibrary.com/central/doi/10.1002/central/CN-01908583/full
15 Gong D, Wu L, Zhang J. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 2020; 5: 352-361
16 Su JR, Li Z, Shao XJ. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91: 415-424.e4
17 Liu W, Zhang Y, Bian X. et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2020; 26: 13-19
18 Wang P, Berzin TM, Glissen Brown JR. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019; 68: 1813-1819
19 Wang P, Liu X, Berzin TM. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 2020; 5: 343-351
20 von Renteln D, Barkun AN. Increasing detection rates for diminutive adenomas: are we on the right track?. Gut 2016; 65: 1056-1057
21 Cai B, Liu Z, Yansong X. et al. Adenoma detection rate in 41,010 patients from Southwest China. Oncol Lett 2015; 9: 2073-2077
22 Gottlieb K, Hussain F. Voting for Image Scoring and Assessment (VISA) - theory and application of a 2 + 1 reader algorithm to improve accuracy of imaging endpoints in clinical trials. BMC Med Imaging 2015; 15: 6

Zusatzmaterial

Zusatzmaterial