Abstract
Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. We evaluated the diagnostic performance of deep learning-based artificial intelligence (AI) in differentiating polypoid lesions using EUS images. The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing. The diagnostic performance was also verified using an external validation cohort and compared with the performance of EUS endoscopists. In the AI development cohort, the diagnostic performance of EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 77.8%, 91.6%, 57.9%, 96.5%, and 89.8%, respectively. In the external validation cohort, the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values were 60.3%, 77.4%, 36.2%, 90.2%, and 74.4%, respectively, for EUS-AI; they were 74.2%, 44.9%, 75.4%, 46.2%, and 65.3%, respectively, for the endoscopists. The accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). This EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.
S984 Pooled Diagnostic Performance of Artificial Intelligence in Endoscopic Ultrasound Image Analysis of Solid Pancreatic Masses: A Descriptive Quantitative Review
