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 Srichawla ⁵ Jyotirmoy Biswas ⁶ Roger Rathna ⁷ 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 ⁸ Gopal Krishna Dhali ^8*

• ¹ Academic Department of Gastroenterology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
• ² School of Medicine and Population Health, The University of Sheffield, Sheffield, England, United Kingdom
• ³ Faculty of Health Sciences, University of Nairobi, Nairobi, Nairobi, Kenya
• ⁴ Institute of Post Graduate Medical Education And Research (IPGMER), Kolkata, West Bengal, India
• ⁵ University of Massachusetts Medical School, Worcester, Massachusetts, United States
• ⁶ College of Medicine & Sagore Dutta Hospital, Calcutta, India
• ⁷ University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
• ⁸ School of Digestive and Liver Diseases, Institute of Post Graduate Medical Education And Research (IPGMER), Kolkata, West Bengal, India

Capsule endoscopy 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 capsule endoscopy (CE)in the diagnosis of small bowel lesions in comparison to CE.Literature searches were performed through PubMed, SCOPUS, and Embasse 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, metaanalyses, 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.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) while 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 to 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) while 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 to 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.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.