Artificial Intelligence Versus Conventional Scoring for Assessing Small Bowel Capsule Endoscopy Cleanliness in Crohn's Disease.

BACKGROUND AND AIMS: Evaluation of cleanliness in capsule endoscopy is essential to validate the exam. However, reliable evaluation of bowel cleanliness remains challenging. The validated KODA score provides robust assessment but is time-consuming and impractical in routine practice. We aimed to evaluate the performance of an AI-based tool (AXAROlite, Augmented Endoscopy) compared with the KODA score for assessing small bowel cleanliness in CD patients.

METHODS

This was a post hoc analysis of a multicenter randomized controlled trial (NCT05117996) including 142 CD patients undergoing SBCE after either a standard PEG-based or simplified clear liquid preparation. All videos were evaluated for cleanliness using the KODA score by trained readers. The same videos were analyzed using the AI-based AXAROlite tool. Correlation, agreement, and diagnostic performance of AXAROlite were compared with the KODA score. Clinical factors influencing AI-assessed cleanliness were also investigated.

RESULTS

AXAROlite and KODA showed strong correlation for whole small bowel (ρ = 0.61; p < 0.001). AXAROlite demonstrated excellent diagnostic accuracy for detecting adequate cleanliness defined as KODA > 2.25 (AUROC = 0.85, 95% CI 0.79-0.92), with an optimal threshold of 72% clean frames (sensitivity 86%, specificity 72%). Mean cleanliness decreased progressively from proximal to distal small bowel with both methods (p = 0.001). The AXROlite score was independently reduced in patients with active disease and a longer small bowel transit time.

CONCLUSIONS

AXAROlite provides a rapid, fully automated, and accurate evaluation of small bowel cleanliness in CD, comparable to the validated KODA score. Its integration into routine workflows may streamline reporting and reduce inter-observer variability.