Capsule endoscopy with artificial intelligence assisted technology: Real world usage of a validated AI model for capsule image review.

Background and study aims: Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence can potentially reduce reading time significantly by reducing the number of images that need human review. OMOM Artificial Intelligence enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real world setting against standard reading methods. Patients and methods: In this single centre retrospective study 40 patient studies performed using the OMOM capsule were analysed first by standard reading methods and later using AI assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared. Results: Overall diagnosis correlated 100% between the 2 reading methods. In a per lesion analysis, 1293 images of significant lesions were identified combining standard and AI assisted reading methods. AI assisted reading captured 1268 (98.1%, 95% CI 97.15-98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% CI 84.2-87.9), p <0.001. Mean reading time went from 29.7 minutes by standard reading to 2.3 minutes by AI assisted reading (p <0.001). An average time saving of 27.4 minutes per study. Time of first caecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, p =0.68). Bowel cleansing evaluation agreed in 97.4% (r=0.805 p<0.001). Conclusion: AI assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.