Diagnostic performance of real-time artificial intelligence using deep learning analysis of endoscopic ultrasound videos for gallbladder polypoid lesions.

ABSTRACT: Endoscopic ultrasound (EUS) is accurate for diagnosing gallbladder (GB) polyps but is limited by subjective interpretation and operator expertise. Although artificial intelligence (AI) has been applied to still EUS images of GB polyps, its application to EUS videos, which provide richer diagnostic data, remains unexplored. This study evaluated the diagnostic performance of AI models in analyzing EUS videos for GB polyp assessment. EUS videos of patients with histologically confirmed GB polyps were divided into training and validation cohorts. Segmentation models (Attention U-Net, Residual U-Net, and deep understanding convolutional kernel [DUCK] net) identified polyp regions, followed by classification into neoplastic and non-neoplastic polyps using classification models (EfficientNet-B2,

SUBMITTER: Choi YH 

PROVIDER: S-EPMC12764828 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature