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Meta-Analysis
. 2025 May;40(5):1105-1118.
doi: 10.1111/jgh.16931. Epub 2025 Mar 13.

Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis

Arkadeep Dhali  1   2 , Vincent Kipkorir  3 , Rick Maity  4 , Bahadar S Srichawla  5 , Jyotirmoy Biswas  6 , Roger B Rathna  7 , Hareesha Rishab Bharadwaj  8 , Ibsen Ongidi  3 , Talha Chaudhry  3 , Gisore Morara  3 , Maryann Waithaka  3 , Clinton Rugut  3 , Miheso Lemashon  3 , Isaac Cheruiyot  3 , Daniel Ojuka  3 , Sukanta Ray  4 , Gopal Krishna Dhali  4
Affiliations
Meta-Analysis

Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis

Arkadeep Dhali et al. J Gastroenterol Hepatol. 2025 May.

Abstract

Background: Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI-assisted CE in the diagnosis of small bowel lesions in comparison to CE.

Methods: Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta-analyses, and case series reporting outcomes on AI-assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta-analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta-Analysis v4.0 (Biostat Inc.) was used for the analysis of the data.

Results: A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925-0.988), whereas for AI-assisted CE, it was 0.9185 (95% CI: 0.9138-0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786-0.934) compared with AI-assisted CE at 0.9239 (95% CI: 0.8648-0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976-0.987), whereas AI-assisted CE had a PPV of 0.8928 (95% CI: 0.7554-0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996-0.999) compared with 0.5367 (95% CI: 0.5244-0.5492) for AI-assisted CE. Negative predictive values were higher in AI-assisted CE at 0.9425 (95% CI: 0.9389-0.9462) versus 0.760 (95% CI: 0.577-0.943) for conventional CE.

Conclusion: AI-assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.

Keywords: artificial intelligence; bowel; capsule endoscopy; diagnosis; small intestine.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) flowchart for included studies.
FIGURE 2
FIGURE 2
Pooled accuracy for AI‐assisted capsule endoscopy.
FIGURE 3
FIGURE 3
Pooled sensitivity for AI‐assisted capsule endoscopy.
FIGURE 4
FIGURE 4
Pooled specificity for AI‐assisted capsule endoscopy.
FIGURE 5
FIGURE 5
Pooled positive predictive value for AI‐assisted capsule endoscopy.
FIGURE 6
FIGURE 6
Pooled negative predictive value for AI‐assisted capsule endoscopy.
FIGURE 7
FIGURE 7
Risk of bias assessment using the RoBANS II tool.
See this image and copyright information in PMC

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