Deep learning-based localization and lesion detection in capsule endoscopy for patients with suspected small-bowel bleeding

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.