tokens: Recently , a subfield in artificial intelligence known as deep learning has produced a set of image analysis techniques that automatically extract relevant features , transforming the field of computer vision [ 23]. Deep neural networks use a data - driven approach to learn multi - level representations of data , allowing for comprehensive image analysis and classification [ 24]. These techniques are being increasingly applied to medical imaging to assist radiologists and pathologists [ 25]. In gastroenterology , previous studies have already used deep neural networks to classify colorectal polyps on biopsy and colonoscopy images [ 26 - 28 ] , intraductal papillary mucinous neoplasms in MRI images [ 29 ] , and diabetic retinopathy in retinal fundus photographs [ 30]. For CD in particular , large video datasets captured during endoscopies have facilitated quantitative analysis with deep learning [ 31 , 32]. However , endoscopic classification is for the most part not used for confirming the diagnosis of CD .