Improved Efficiency and Lesion Detection in Small Bowel Capsule Endoscopy Using the Open‐Source Artificial Intelligence Model SEE‐AI

ABSTRACT Objectives Small bowel capsule endoscopy (CE) produces lengthy videos that are time‐consuming to review and susceptible to missed lesions. We evaluated whether an open‐source, pretrained artificial intelligence (AI) model (SEE‐AI) could improve diagnostic performance and interpretation efficiency compared with conventional reading. Methods We retrospectively analyzed 249 PillCam SB3 examinations performed between 2007 and 2022 at six hospitals, using a two‐reader crossover design. SEE‐AI (confidence threshold 0.1) generated annotated videos with bounding boxes for eight lesion categories. The primary endpoints were sensitivity for lesion detection on a per‐lesion and per‐patient basis. Secondary endpoints included specificity, predictive values, overall accuracy, and reading time. A prespecified subgroup analysis evaluated cases of suspected small‐bowel bleeding (SSBB), focusing on Saurin P1+P2 hemorrhagic lesions. Results Across 1550 adjudicated lesions, AI‐assisted reading demonstrated higher sensitivity than conventional reading (per‐lesion: 98.8% [1532/1550] vs. 86.4% [1339/1550]; per‐patient: 99.1% [464/468] vs. 80.3% [376/468]; both p < 0.0001). The mean reading time decreased from 17.9 to 13.7 min ( p < 0.0001). In SSBB cases ( n = 131), sensitivity for P1+P2 lesions improved on both a per‐lesion basis (98.2% [439/447] vs. 82.8% [370/447]) and per‐patient basis (98.6% [145/147] vs. 73.5% [108/147]), with a shorter reading time (14.1 vs. 18.0 min; all p < 0.0001). Conclusions In this multicenter evaluation, SEE‐AI significantly improved lesion detection and reduced reading time for CE interpretation, including SSBB cases, while maintaining openness and reproducibility. AI‐assisted reading may reduce clinicians’ workload and support the adoption of SEE‐AI as a practical tool ― and a potential future standard of care ― for small bowel CE. Trial Registration N/A.