Real-Life Clinical Validation of Artificial Intelligence-Assisted Detection and Differentiation of Pleomorphic Lesions in Capsule Endoscopy.

Introduction: Capsule endoscopy (CapE) is a minimally invasive procedure designed for small bowels' evaluation. However, prolonged reading times and a risk of missing clinically significant findings limit its potential. Prospective clinical validation studies of artificial intelligence (AI) for CapE remain scarce. Furthermore, existing studies focus on lesion detection, without addressing lesion differentiation.

Methods: The aim of a multicenter prospective validation study was to compare AI-assisted reading with conventional CapE reading. Three hundred thirty CapE videos from 3 devices across 7 centers and 4 countries were included. After conventional reading reports, AI-assisted reading was performed by an independent expert using a deep learning model to detect and differentiate pleomorphic small bowel lesions. Both reports were reviewed by an expert from an independent center, which decided in discrepant cases. AI-assisted and standard readings were evaluated through their accuracy, sensitivity, specificity, positive and negative predictive value, and small bowel lesion detection rate.

Results: AI-assisted reading detected 605 of 635 lesions identified by expert-based consensus, whereas standard reading identified 354 lesions. AI-assisted reading outperformed standard reading, with small bowel lesion detection rate of 96.1% vs 76.3% and sensitivity of 97.5% vs 78.2%. AI-assisted reading had a mean examination reading time of 203 seconds per examination.

Discussion: This was the first multicentric study proving AI-assisted CapE reading superiority compared with conventional reading. The inclusion of videos from multiple devices addresses the interoperability challenge, whereas including patients from 4 countries and 2 different continents assures a diverse demographic context. AI achieved gastroenterologist-level identification of small-bowel lesions, surpassing conventional reading methods in both lesion detection and characterization.