Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and early and resection detection of colorectal neoplastic lesions can significantly reduce colorectal cancer morbidity and mortality. In order to improve the diagnostic accuracy of endoscopy for colorectal lesions, many endoscopic techniques, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, pigment endoscopy, confocal laser endoscopy, and endocytoscopy(EC), are applied clinically. However, with the increasing number of endoscopic resection, the costs associated with the pathological diagnosis of endoscopic resection and resection specimens increase year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to identify the nature of the lesion during colonoscopy. Leveraging deep neural networks, AI systems support both computer-aided detection (CADe) and computer-aided classification (CADx). CADe specifically focuses on identifying polyps in colonoscopy, with the goal of reducing adenoma miss rates. Hovever, CADx can predict the pathology of the lesion based on the surface condition of the lesion. Endocytoscopy is a kind of ultra-high magnification endoscopy. But it is not something that can be easily mastered by endoscopic doctors. The investigators have previously developed an artificial intelligence system that can assist in endocytoscopy. The investigators plan to conduct a prospective, multicenter clinical trial to verify the accuracy of this CADx in predicting the histological characteristics of colorectal lesions during real-time endocytoscopy.

Colonoscopy is currently the gold standard of screening for CRC. Endocytoscopy is a kind of ultra-high magnification endoscopy. Combined with chemical staining and narrow band imaging technology, endoscopists can observe the cell nucleus morphology, gland tube morphology and microvascular morphology with the naked eye, so as to avoid pathological examination and realize the purpose of real-time biopsy in the body. However, the judgment of endocytoscopic images needs a lot of experience to improve the judgment accuracy. Moreover, endoscopists have certain subjective judgments and errors in the process of judging the results. Therefore, artificial intelligence (AI) is proposed for computer-assisted diagnosis in clinic to solve this problem. At present, the available artificial intelligence systems for assisting endoscopists in diagnosing using endocytoscopy are still relatively scarce, and they are based on traditional machine learning methods, which still have certain limitations in terms of accuracy. With the continuous development of computer vision technology, deep learning has been widely applied in the development of endoscopic assistance diagnosis systems. Therefore, the investigators developed a CADx system trained using deep learning to assist endoscopists in making diagnoses when using endocytoscopy. This CADx system can predict the captured endocytoscopy images in real time and display the prediction results, which can assist endoscopists in providing diagnostic references.

However, currently this CADx technology has not yet undergone prospective clinical validation in the clinical setting. The investigators plan to conduct a prospective clinical trial to validate the accuracy of CADx for prediction of colorectal lesions histology in real-time endocytoscopy. This study will prospectively collect the lesions that meet the inclusion and exclusion criteria. After the endoscopists make the diagnosis through endoscopic images and CADx and then undergo endoscopic resection or surgical resection followed by pathological diagnosis, they will compare the artificial intelligence diagnosis results with the gold standard pathological results, and summarize the diagnostic accuracy of this artificial intelligence-assisted diagnostic system for the colorectal lesions.

The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of colorectal lesions histology in real-time endocytoscopy.

Inclusion Criteria:

• Patients who have at least one colorectal lesion detected during endocytoscopy
• Consent obtained for the study

Exclusion Criteria:

• lesions lacking high-quality images;
• Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
• Submucosal tumors;
• Pathological diagnosis of inflammatory polyps, Peutz-Jeghers polyps, juvenile polyps, lymphoma and other special pathological types.

Patients with one or more colorectal lesions detected during endocytoscopy will be included in the study. The rest of the inclusion and exclusion criteria are as described.