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. 2023 Feb 1;85(2):80-91.
doi: 10.1097/MS9.0000000000000079. eCollection 2023 Feb.

The effectiveness of real-time computer-aided and quality control systems in colorectal adenoma and polyp detection during colonoscopies: a meta-analysis

Muhammad Fawad Aslam  1 , Shehar Bano  2 , Mariam Khalid  3 , Zouina Sarfraz  2 , Azza Sarfraz  4 , Muzna Sarfraz  5 , Karla Robles-Velasco  6 , Miguel Felix  6   7 , Kitson Deane  8 , Ivan Cherrez-Ojeda  5
Affiliations

The effectiveness of real-time computer-aided and quality control systems in colorectal adenoma and polyp detection during colonoscopies: a meta-analysis

Muhammad Fawad Aslam et al. Ann Med Surg (Lond). .

Abstract

This meta-analysis aims to quantify the effectiveness of artificial intelligence (AI)-supported colonoscopy compared to standard colonoscopy in adenoma detection rate (ADR) differences with the use of computer-aided detection and quality control systems. Moreover, the polyp detection rate (PDR) intergroup differences and withdrawal times will be analyzed.

Methods: This study was conducted adhering to PRISMA guidelines. Studies were searched across PubMed, CINAHL, EMBASE, Scopus, Cochrane, and Web of Science. Keywords including the following 'Artificial Intelligence, Polyp, Adenoma, Detection, Rate, Colonoscopy, Colorectal, Colon, Rectal' were used. Odds ratio (OR) applying 95% CI for PDR and ADR were computed. SMD with 95% CI for withdrawal times were computed using RevMan 5.4.1 (Cochrane). The risk of bias was assessed using the RoB 2 tool.

Results: Of 2562 studies identified, 11 trials were included comprising 6856 participants. Of these, 57.4% participants were in the AI group and 42.6% individuals were in in the standard group. ADR was higher in the AI group compared to the standard of care group (OR=1.51, P=0.003). PDR favored the intervened group compared to the standard group (OR=1.89, P<0.0001). A medium measure of effect was found for withdrawal times (SMD=0.25, P<0.0001), therefore with limited practical applications.

Conclusion: AI-supported colonoscopies improve PDR and ADR; however, no noticeable worsening of withdrawal times is noted. Colorectal cancers are highly preventable if diagnosed early-on. With AI-assisted tools in clinical practice, there is a strong potential to reduce the incidence rates of cancers in the near future.

Keywords: adenoma; colorectal; meta-analysis; polyps; trials; withdrawal time.

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Conflict of interest statement

All authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart
Figure 2
Figure 2
Adenoma detection rate forest plot and risk of bias. Odds ratio=1.51, 95% CI=1.15–1.99. Heterogeneity: Tau2=0.18; χ 2=60.72, df=10 (P<0.00001); I 2=84%. Test for overall effect: Z=2.94 (P=0.003).
Figure 3
Figure 3
Polyp detection rate forest plot. Odds ratio=1.89, 95% CI=1.66–2.15. Heterogeneity: Tau2=0.01; χ 2=12.22, df=9 (P=0.20); I 2=26%. Test for overall effect: Z=9.59 (P<0.00001).
Figure 4
Figure 4
Standardized mean difference for withdrawal time, forest plot. SMD=0.25, 95% CI=0.2–0.31. Heterogeneity: χ 2=224.14, df=7 (P<0.00001); I 2=97%. Test for overall effect: Z=9.48 (P<0.00001).
Figure 5
Figure 5
Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies.
Figure 6
Figure 6
Funnel plot depicting publication bias. OR, odds ratio.
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