Materials and Methods: A CNN was developed based on 72 DBE exams. A total of 7925 images were included, 2535 images containing protruding lesions (polyps, epithelial tumors, subepithelial lesions and nodules), and the remaining showed normal mucosa (n=5390). A training dataset was constructed for development of the network (80% of the total image pool). The performance of the CNN was evaluated using an independent validation dataset (20% of total image pool). The output provided by the network was compared to a consensus classification provided by two experts in DBE. The sensitivity, specificity, accuracy, positive and negative predictive values (PPV and NPV, respectively), and area under the curve (AUC) were calculated.Results: Our model automatically detected SB protruding lesions with an accuracy of 97.3%. The sensitivity, specificity, PPV and NPV were, respectively, 97.0%, 97.4%, 94.6%, and 98.6%. The AUC was 1.00. The CNN completed the analysis of validation dataset in 7 seconds, at a rate of approximately 239 frames per second.Discussion: We developed a pioneer AI algorithm for automatic detection of enteric protruding lesions during DBE. The development of these tools may enhance the diagnostic yield of deep enteroscopy techniques, with significant impact in the management of these patients.