Background: There are multiple developed and developing artificial intelligence (AI) tools for real-time detection of colorectal polyps. However, this technology has yet to be developed for identification of polypoid and flat lesions in patients with inflammatory bowel disease (IBD). Not all polypoid lesions in IBD are dysplastic, with serrated epithelial changes and pseudopolyps frequently occurring in this patient population. There are also challenges identifying dysplasia in the setting of background inflammation. This study aims to train and test a YOLO (“You Only Look Once”) computer aided detection system (CADe) for automated detection of polypoid lesions in patients with IBD. Methods: An original CADe model was pretrained using 8,000 endoscopic images and trained with 2,268 annotated white light (WL) colon polyps from patients without IBD. First, this original CADe system was tested in our entire dataset of 2016 unlabeled WL IBD polypoid lesions which were annotated into 5 categories based on pathology: dysplastic, non-dysplastic, pseudopolyps, serrated changes (SECs) and sessile serrate adenomas (SSAs). Then, all IBD polyps were hand-labeled with bounding boxes by 8 physicians. A training dataset comprising 80% of the total pool of IBD polypoid lesions was used to re-train the original model and the additional 20% was used for iterations and testing. We measured sensitivity, positive predictive value (PPV), false positive rate (FPR), and accuracy (F1 score) before and after re-training the model with IBD lesions. The object detection model used was YOLO version 4 with a scaling cross stage network approach (ScaledYOLOv4). Results: When the original CADe model was first tested in IBD polyp images, the performance metrics were highest for dysplastic polyps and lowest for pseudopolyps and SECs likely because these IBD lesions were never seen before by the original CADe trained with non-IBD polyps only. The overall sensitivity, PPV, FPR and F1 was as follows: 0.50, 0.97, 1.7% and 0.64. After re-training the system with IBD polypoid lesions, the sensitivity improved for all polyp types while maintaining a high PPV and low FPR of 0.95, 0.95, 5% and 0.95 respectively. From the 9 lesions missed by IBD-CADe in the test set, 3 had a corresponding Mayo Score of 0, 2 had a score of 1, 2 had a score of 2 and 2 had a score of 3 indicating no significant difference in performance detection per level of background inflammation. Conclusion(s): We successfully developed the first IBD-CADe system capable of detecting polypoid lesions in IBD including serrated lesions and pseudopolyps even with background inflammation. The development of this tool is the first step to the creation of several technologies that are inclusive to patients with IBD, increasing polyp detection in this patient cohort and aiding the endoscopist in automated detection of polypoid dysplasia.