Diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps: a meta-analysis_West China Medical Journal

Authors：

HAN Wei , WEI Yan , ZHAO Li ,  ZHANG Zhe , ZHOU Jinchi , ZHAO Shuguang

Department of Gastroenterology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi 710038, P. R. China;

Corresponding author：

ZHANG Zhe, Email: 543337027@qq.com

Keywords：

Narrow-band imaging; artificial intelligence; adenomatous polyps; convolutional neural network; deep learning; meta-analysis

DOI：

10.7507/1002-0179.202007314

Video：

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Objective To systematically evaluate the diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps. Methods Pubmed, Embase, Web of Science, Cochrane Library, SinoMed, China National Knowledge Infrastructure, Chongqing VIP and Wanfang databases were searched. The diagnostic trials of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps were comprehensively searched. The search time limit was from January 1, 2000 to October 31, 2022. The included studies were evaluated according to the Quality Assessment of Diagnostic Accuracy Studies-2, and the data were meta-analysed with RevMan 5.3, Meta-Disc 1.4 and Stata 13.0 statistical softwares. Results Finally, 11 articles were included, including 2178 patients. Meta-analysis results of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps showed that the pooled sensitivity was 0.91, the pooled specificity was 0.88, the pooled positive likelihood ratio was 7.41, the pooled negative likelihood ratio was 0.10, the pooled diagnostic odds ratio was 76.45, and the area under the summary receiver operating characteristic curve was 0.957. Among them, 5 articles reported the diagnosis of small adenomatous polyps (diameter <5 mm) by the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system. The results showed that the pooled sensitivity and the pooled specificity were 0.93 and 0.91, respectively, and the area under the summary receiver operating characteristic curve was 0.971. Five articles reported the accuracy of endoscopic diagnosis for adenomatous polyps of those with insufficient experience. The results showed that the pooled sensitivity and the pooled specificity were 0.84 and 0.76, respectively. The area under the summary receiver operating characteristic curve was 0.848. Compared with the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system, the difference was statistically significant (Z=1.979, P=0.048). Conclusion The artificial intelligence assisted narrow-band imaging endoscopy diagnostic system has a high diagnostic accuracy, which can significantly improve the diagnostic accuracy for colorectal adenomatous polyps of those with insufficient endoscopic experience, and can effectively compensate for the adverse impact of their lack of endoscopic experience.

Citation： HAN Wei, WEI Yan, ZHAO Li, ZHANG Zhe, ZHOU Jinchi, ZHAO Shuguang. Diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps: a meta-analysis. West China Medical Journal, 2023, 38(1): 72-78. doi: 10.7507/1002-0179.202007314 Copy

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3.	Lee JY, Jeong J, Song EM, et al. Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Sci Rep, 2020, 10(1): 8379.
4.	Li H, Hou X, Lin R, et al. Advanced endoscopic methods in gastrointestinal diseases: a systematic review. Quant Imaging Med Surg, 2019, 9(5): 905-920.
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6.	Luo Y, Zhang Y, Liu M, et al. Artificial intelligence-assisted colonoscopy for detection of colon polyps: a prospective, randomized cohort study. J Gastrointest Surg, 2021, 25(8): 2011-2018.
7.	Xu L, He X, Zhou J, et al. Artificial intelligence-assisted colonoscopy: a prospective, multicenter, randomized controlled trial of polyp detection. Cancer Med, 2021, 10(20): 7184-7193.
8.	Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut, 2019, 68(1): 94-100.
9.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
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12.	Mori Y, Kudo SE, Chiu PW, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy, 2016, 48(12): 1110-1118.
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14.	Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med, 2018, 169(6): 357-366.
15.	Renner J, Phlipsen H, Haller B, et al. Optical classification of neoplastic colorectal polyps - a computer-assisted approach (the COACH study). Scand J Gastroenterol, 2018, 53(9): 1100-1106.
16.	Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology, 2018, 154(3): 568-575.
17.	Jin EH, Lee D, Bae JH, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology, 2020, 158(8): 2169-2179.e8.
18.	Mori Y, Kudo SE, East JE, et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc, 2020, 92(4): 905-911.e1.
19.	Song EM, Park B, Ha CA, et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep, 2020, 10(1): 30.
20.	Racz I, Horvath A, Kranitz N, et al. Artificial intelligence-based colorectal polyp histology prediction by using narrow-band image-magnifying colonoscopy. Clin Endosc, 2022, 55(1): 113-121.
21.	Gono K. Narrow band imaging: technology basis and research and development history. Clin Endosc, 2015, 48(6): 476-480.
22.	Doyama H, Nakanishi H, Yao K. Image-enhanced endoscopy and its corresponding histopathology in the stomach. Gut Liver, 2021, 15(3): 329-337.
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24.	Yaacob H, Ikhwan SM, Hashim MN, et al. Prospective diagnostic study on the use of narrow-band imaging on suspicious lesions during colonoscopy examination. Asian J Endosc Surg, 2018, 11(4): 318-324.
25.	Atkinson NSS, Ket S, Bassett P, et al. Narrow-band imaging for detection of neoplasia at colonoscopy: a meta-analysis of data from individual patients in randomized controlled trials. Gastroenterology, 2019, 157(2): 462-471.
26.	She Y, Jin Z, Wu J, et al. Development and validation of a deep learning model for non-small cell lung cancer survival. JAMA Netw Open, 2020, 3(6): e205842.
27.	Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology, 2018, 155(4): 1069-1078.e8.
28.	Park HC, Kim YJ, Lee SW. Adenocarcinoma recognition in endoscopy images using optimized convolutional neural networks. Appl Sci, 2020, 10(5): 1650.
29.	Hu Z, Tang J, Wang Z, et al. Deep learning for image-based cancer detection and diagnosis - a survey. Pattern Recogn, 2018, 83: 134-149.
30.	Bjørsum-Meyer T, Koulaouzidis A, Baatrup G. Comment on “Artificial intelligence in gastroenterology: a state-of-the-art review”. World J Gastroenterol, 2022, 28(16): 1722-1724.
31.	Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of diminutive colorectal polyps in endoscopic images: systematic review and meta-analysis of diagnostic test accuracy. J Med Internet Res, 2021, 23(8): e29682.

1. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2. Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy?. Gastroenterology, 2017, 153(6): 1460-1464.e1.
3. Lee JY, Jeong J, Song EM, et al. Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Sci Rep, 2020, 10(1): 8379.
4. Li H, Hou X, Lin R, et al. Advanced endoscopic methods in gastrointestinal diseases: a systematic review. Quant Imaging Med Surg, 2019, 9(5): 905-920.
5. Poon CCY, Jiang Y, Zhang R, et al. AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices. NPJ Digit Med, 2020, 3: 73.
6. Luo Y, Zhang Y, Liu M, et al. Artificial intelligence-assisted colonoscopy for detection of colon polyps: a prospective, randomized cohort study. J Gastrointest Surg, 2021, 25(8): 2011-2018.
7. Xu L, He X, Zhou J, et al. Artificial intelligence-assisted colonoscopy: a prospective, multicenter, randomized controlled trial of polyp detection. Cancer Med, 2021, 10(20): 7184-7193.
8. Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut, 2019, 68(1): 94-100.
9. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
10. Gross S, Trautwein C, Behrens A, et al. Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification. Gastrointest Endosc, 2011, 74(6): 1354-1359.
11. Kominami Y, Yoshida S, Tanaka S, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc, 2016, 83(3): 643-649.
12. Mori Y, Kudo SE, Chiu PW, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy, 2016, 48(12): 1110-1118.
13. Misawa M, Kudo SE, Mori Y, et al. Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts. Int J Comput Assist Radiol Surg, 2017, 12(5): 757-766.
14. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med, 2018, 169(6): 357-366.
15. Renner J, Phlipsen H, Haller B, et al. Optical classification of neoplastic colorectal polyps - a computer-assisted approach (the COACH study). Scand J Gastroenterol, 2018, 53(9): 1100-1106.
16. Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology, 2018, 154(3): 568-575.
17. Jin EH, Lee D, Bae JH, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology, 2020, 158(8): 2169-2179.e8.
18. Mori Y, Kudo SE, East JE, et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc, 2020, 92(4): 905-911.e1.
19. Song EM, Park B, Ha CA, et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep, 2020, 10(1): 30.
20. Racz I, Horvath A, Kranitz N, et al. Artificial intelligence-based colorectal polyp histology prediction by using narrow-band image-magnifying colonoscopy. Clin Endosc, 2022, 55(1): 113-121.
21. Gono K. Narrow band imaging: technology basis and research and development history. Clin Endosc, 2015, 48(6): 476-480.
22. Doyama H, Nakanishi H, Yao K. Image-enhanced endoscopy and its corresponding histopathology in the stomach. Gut Liver, 2021, 15(3): 329-337.
23. Djinbachian R, Dubé AJ, von Renteln D. Optical diagnosis of colorectal polyps: recent developments. Curr Treat Options Gastroenterol, 2019, 17(1): 99-114.
24. Yaacob H, Ikhwan SM, Hashim MN, et al. Prospective diagnostic study on the use of narrow-band imaging on suspicious lesions during colonoscopy examination. Asian J Endosc Surg, 2018, 11(4): 318-324.
25. Atkinson NSS, Ket S, Bassett P, et al. Narrow-band imaging for detection of neoplasia at colonoscopy: a meta-analysis of data from individual patients in randomized controlled trials. Gastroenterology, 2019, 157(2): 462-471.
26. She Y, Jin Z, Wu J, et al. Development and validation of a deep learning model for non-small cell lung cancer survival. JAMA Netw Open, 2020, 3(6): e205842.
27. Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology, 2018, 155(4): 1069-1078.e8.
28. Park HC, Kim YJ, Lee SW. Adenocarcinoma recognition in endoscopy images using optimized convolutional neural networks. Appl Sci, 2020, 10(5): 1650.
29. Hu Z, Tang J, Wang Z, et al. Deep learning for image-based cancer detection and diagnosis - a survey. Pattern Recogn, 2018, 83: 134-149.
30. Bjørsum-Meyer T, Koulaouzidis A, Baatrup G. Comment on “Artificial intelligence in gastroenterology: a state-of-the-art review”. World J Gastroenterol, 2022, 28(16): 1722-1724.
31. Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of diminutive colorectal polyps in endoscopic images: systematic review and meta-analysis of diagnostic test accuracy. J Med Internet Res, 2021, 23(8): e29682.

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Diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps: a meta-analysis

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