Efficacy and safety of computer-aided detection(CADe) in colonoscopy for colorectal neoplasia detection: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

MA Yanling ^1,2 ,  WEN Yue ^1,2 , LU Chunyan ^1,2 , LIAO Jing ^1,2

1. Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

WEN Yue, Email: wenyue123289006@163.com

Keywords：

Computer assisted detection; Artificial intelligence; Deep learning models; Colonoscopy; Polyps; Adenoma; Diagnosis; Meta-analysis

DOI：

10.7507/1672-2531.202312193

Video：

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Objective To systematically evaluate the effectiveness and safety of computer-aided detection (CADe) and conventional colonoscopy in identifying colorectal adenomas and polyps. Methods The PubMed, Embase, Cochrane Library, Web of Science, WanFang Data, VIP, and CNKI databases were electronically searched to collect randomized controlled trials (RCTs) comparing the effectiveness and safety of CADe assisted colonoscopy and conventional colonoscopy in detecting colorectal tumors from 2014 to April 2023. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included literature. Meta-analysis was performed by RevMan 5.3 software. Results A total of 9 studies were included, with a total of 6 393 patients. Compared with conventional colonoscopy, the CADe system significantly improved the adenoma detection rate (ADR) (RR=1.22, 95% CI 1.10 to 1.35, P<0.01) and polyp detection rate (PDR) (RR=1.19, 95% CI 1.04 to 1.36, P=0.01). It also reduced the missed diagnosis rate (AMR) of adenomas (RR=0.48, 95% CI 0.34 to 0.67, P<0.01) and the missed diagnosis rate (PMR) of polyps (RR=0.39, 95% CI 0.25 to 0.59, P<0.01). The PDR of proximal polyps significantly increased, while the PDR of ≤ 5mm polyps slightly increased, but the PDR of >10mm and pedunculated polyps significantly decreased; The AMR of the cecum, transverse colon, descending colon, and sigmoid colon was significantly reduced. There was no statistically significant difference in the withdrawal time between the two groups. Conclusion The computer-aided detection system can increase the detection rate of adenomas and polyps, and reduce the missed diagnosis rate. The detection rate of polyps is related to their location, size, and shape, while the missed diagnosis rate of adenomas is related to their location.

1.	李倩倩, 王军, 赵越, 等. 结直肠息肉发生相关危险因素的研究现状. 医学综述, 2020, 26(16): 3196-3200.
2.	陈星宇, 孔令斌. 结直肠腺瘤性息肉癌变相关因素及机制研究进展. 中华肿瘤防治杂志, 2019, 26(5): 354-358.
3.	Corley DA, Jensen CD, Marks AR, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med, 2014, 370(14): 1298-1306.
4.	Anderson R, Burr NE, Valori R. Causes of post-colonoscopy colorectal cancers based on world endoscopy organization system of analysis. Gastroenterology, 2020, 158(5): 1287-1299.
5.	Zhao S, Wang S, Pan P, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology, 2019, 156(6): 1661-1674.
6.	Schottinger JE, Jensen CD, Ghai NR, et al. Association of physician adenoma detection rates with postcolonoscopy colorectal cancer. JAMA, 2022, 327(21): 2114-2122.
7.	Li J, Lu J, Yan J, et al. Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol, 2021, 33(8): 1041-1048.
8.	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.
9.	Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology, 2016, 150(7): 1531-1532.
10.	Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology, 2018, 154(3): 568-575.
11.	Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy. Gastroenterology, 2017, 153(6): 1460-1464.
12.	Repici A, Badalamenti M, Maselli R, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology, 2020, 159(2): 512-520.
13.	Glissen Brown JR, Mansour NM, Wang P, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a united states multi-center randomized tandem colonoscopy study (CADeT-CS trial). Clin Gastroenterol Hepatol, 2022, 20(7): 1499-1507.
14.	Wang P, Liu P, Glissen Brown JR, et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology, 2020, 159(4): 1252-1261.
15.	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(4): 343-351.
16.	Kamba S, Tamai N, Saitoh I, et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol, 2021, 56(8): 746-757.
17.	Liu WN, Zhang YY, Bian XQ, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol, 2020, 26(1): 13-19.
18.	Shaukat A, Lichtenstein DR, Somers SC, et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology, 2022, 163(3): 732-741.
19.	Ahmad A, Wilson A, Haycock A, et al. Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study. Endoscopy, 2023, 55(4): 313-319.
20.	Rondonotti E, Di Paolo D, Rizzotto ER, et al. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy, 2022, 54(12): 1171-1179.
21.	Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology- promises and pitfalls. Am J Gastroenterol, 2019, 114(3): 422-428.
22.	Gimeno-García AZ, Hernández Negrin D, Hernández A, et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc, 2023, 97(3): 528-536.
23.	Wang H, Liang Z, Li LC, et al. An adaptive paradigm for computer-aided detection of colonic polyps. Phys Med Biol, 2015, 60(18): 7207-7228.
24.	Roth HR, Lu L, Liu J, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging, 2016, 35(5): 1170-1181.
25.	Hassan C, Wallace MB, Sharma P, et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut, 2020, 69(5): 799-800.
26.	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(10): 1813-1819.
27.	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.
28.	Thomas J, Ravichandran R, Nag A, et al. Advancing colorectal cancer screening: a comprehensive systematic review of artificial intelligence (AI)-assisted versus routine colonoscopy. Cureus, 2023, 15(9): e45278.
29.	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(4): 352-361.
30.	Deliwala SS, Hamid K, Barbarawi M, et al. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis, 2021, 36(11): 2291-2303.
31.	Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol, 2019, 4(1): 71-80.
32.	Matsuda T, Fujii T, Sano Y, et al. Randomised comparison of postpolypectomy surveillance intervals following a two-round baseline colonoscopy: the Japan Polyp Study Workgroup. Gut, 2020, 70(8): 1469-1478.

1. 李倩倩, 王军, 赵越, 等. 结直肠息肉发生相关危险因素的研究现状. 医学综述, 2020, 26(16): 3196-3200.
2. 陈星宇, 孔令斌. 结直肠腺瘤性息肉癌变相关因素及机制研究进展. 中华肿瘤防治杂志, 2019, 26(5): 354-358.
3. Corley DA, Jensen CD, Marks AR, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med, 2014, 370(14): 1298-1306.
4. Anderson R, Burr NE, Valori R. Causes of post-colonoscopy colorectal cancers based on world endoscopy organization system of analysis. Gastroenterology, 2020, 158(5): 1287-1299.
5. Zhao S, Wang S, Pan P, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology, 2019, 156(6): 1661-1674.
6. Schottinger JE, Jensen CD, Ghai NR, et al. Association of physician adenoma detection rates with postcolonoscopy colorectal cancer. JAMA, 2022, 327(21): 2114-2122.
7. Li J, Lu J, Yan J, et al. Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol, 2021, 33(8): 1041-1048.
8. 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.
9. Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology, 2016, 150(7): 1531-1532.
10. Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology, 2018, 154(3): 568-575.
11. Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy. Gastroenterology, 2017, 153(6): 1460-1464.
12. Repici A, Badalamenti M, Maselli R, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology, 2020, 159(2): 512-520.
13. Glissen Brown JR, Mansour NM, Wang P, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a united states multi-center randomized tandem colonoscopy study (CADeT-CS trial). Clin Gastroenterol Hepatol, 2022, 20(7): 1499-1507.
14. Wang P, Liu P, Glissen Brown JR, et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology, 2020, 159(4): 1252-1261.
15. 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(4): 343-351.
16. Kamba S, Tamai N, Saitoh I, et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol, 2021, 56(8): 746-757.
17. Liu WN, Zhang YY, Bian XQ, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol, 2020, 26(1): 13-19.
18. Shaukat A, Lichtenstein DR, Somers SC, et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology, 2022, 163(3): 732-741.
19. Ahmad A, Wilson A, Haycock A, et al. Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study. Endoscopy, 2023, 55(4): 313-319.
20. Rondonotti E, Di Paolo D, Rizzotto ER, et al. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy, 2022, 54(12): 1171-1179.
21. Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology- promises and pitfalls. Am J Gastroenterol, 2019, 114(3): 422-428.
22. Gimeno-García AZ, Hernández Negrin D, Hernández A, et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc, 2023, 97(3): 528-536.
23. Wang H, Liang Z, Li LC, et al. An adaptive paradigm for computer-aided detection of colonic polyps. Phys Med Biol, 2015, 60(18): 7207-7228.
24. Roth HR, Lu L, Liu J, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging, 2016, 35(5): 1170-1181.
25. Hassan C, Wallace MB, Sharma P, et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut, 2020, 69(5): 799-800.
26. 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(10): 1813-1819.
27. 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.
28. Thomas J, Ravichandran R, Nag A, et al. Advancing colorectal cancer screening: a comprehensive systematic review of artificial intelligence (AI)-assisted versus routine colonoscopy. Cureus, 2023, 15(9): e45278.
29. 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(4): 352-361.
30. Deliwala SS, Hamid K, Barbarawi M, et al. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis, 2021, 36(11): 2291-2303.
31. Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol, 2019, 4(1): 71-80.
32. Matsuda T, Fujii T, Sano Y, et al. Randomised comparison of postpolypectomy surveillance intervals following a two-round baseline colonoscopy: the Japan Polyp Study Workgroup. Gut, 2020, 70(8): 1469-1478.

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