The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. The main question it aims to answer are to demonstrate whether the newly developed CADx system has a high-level diagnostic accuracy in predicting characterization of colorectal ulcerative diseases.

It is a multi-center, retrospective study. The study retrospectively collected colonoscopy images and videos of colorectal ulcers (including colorectal cancer, Crohn's disease, Ulcerative colitis, Intestinal tuberculosis and ischemic enteritis). A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. A CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set.

The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. Endoscopic photos and videos will be retrieved from existing database in the study centers. For each colorectal ulcer, different endoscopic views will be captures.Relevant baseline demographics, laboratory reports, imaging reports, endoscopy reports and histopathology results will be collected for analysis. The location, size and morphology of each colonic lesion will be recorded. The diagnosis of all colorectal ulcerative disease was comprehensively evaluated by independent pathologists and gastroenterologists. In our study, we will focus on the following subtypes of colorectal ulcerative lesions:

1. colorectal cancer (CA);
2. Crohn's disease (CD);
3. Ulcerative colitis (UC);
4. intestinal tuberculosis (ITB);
5. ischemic colitis (IC). All data will be de-identified before central processing to ensure confidentiality. A project-specific serial number will be used to represent each individual subject. All clinical data and de-identified endoscopic images will be kept confidential and will not be shared with any third party.

A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. The endoscopic images and videos will be prepared to train the convoluted neural network and recurrent neural network by selecting appropriate regions of interest (ROI). Multiple ROI within the same colorectal ulcerative disease will be collected to reduce selection bias. Annotation and validation of endoscopic images will be performed by research team. The images will be further segmented into tiles of the same size for further processing. Deep learning algorithms will be applied to learn and extract features on the image and video data. We will develop the recurrent convolutional network to leverage the complementary information of visual and temporal features extracted from the video. Validation data are also created under the same principle which enable cross-validation for model accuracy.

Inclusion Criteria:

1. They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine;
2. They have endoscopic images and videos captured and stored during colonoscopy which are available to be retrieved;
3. They have the complete medical records and clear diagnosis.

Exclusion Criteria:

1)Poor quality endoscopic images and videos defined as:

1. Incomplete visualization of the colorectal ulcer due to technical reasons (e.g. out-of-focus, motion-blurred or insufficient illumination);
2. Artifacts due to mucus, air bubbles, stool, or blood. 2)Obscured view due to poor bowel preparation; 3)Incomplete medical record; 4)Prior history of intestinal resection, fistula, or anastomosis.

They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine.