Abstract
BACKGROUND: Small-bowel capsule endoscopy (SBCE) is widely used to evaluate obscure gastrointestinal bleeding; however, its interpretation is time-consuming and reader-dependent. Although artificial intelligence (AI) has emerged to address these limitations, few models simultaneously perform small-bowel (SB) localization and abnormality detection. AIM: To develop an AI model that automatically distinguishes the SB from the stomach and colon and diagnoses SB abnormalities. METHODS: We developed an AI model using 87005 CE images (11925, 33781, and 41299 from the stomach, SB, and colon, respectively) for SB localization and 28405 SBCE images (1337 erosions/ulcers, 126 angiodysplasia, 494 bleeding, and 26448 normal) for abnormality detection. The diagnostic performances of AI-assisted reading and conventional reading were compared using 32 SBCE videos in patients with suspicious SB bleeding. RESULTS: Regarding organ localization, the AI model achieved an area under the receiver operating characteristic curve (AUC) and accuracy exceeding 0.99 and 97%, respectively. For SB abnormality detection, the performance was as follows: Erosion/ulcer: 99.4% accuracy (AUC, 0.98); angiodysplasia: 99.8% accuracy (AUC, 0.99); bleeding: 99.9% accuracy (AUC, 0.99); normal: 99.3% accuracy (AUC, 0.98). In external validation, AI-assisted reading (8.7 minutes) was significantly faster than conventional reading (53.9 minutes; P < 0.001). The SB localization accuracies (88.6% vs 72.7%, P = 0.07) and SB abnormality detection rates (77.3% vs 77.3%, P = 1.00) of the conventional reading and AI-assisted reading were comparable. CONCLUSION: Our AI model decreased SBCE reading time and achieved performance comparable to that of experienced endoscopists, suggesting that AI integration into SBCE reading enables efficient and reliable SB abnormality detection.