Colorectal cancer (colorectal cancer, CRC) is the third most common malignant tumor globally and the second leading cause of cancer-related deaths. Colonoscopy is considered the preferred method for screening colorectal cancer; early detection and removal of colorectal neoplasms can significantly reduce the incidence and mortality of colorectal cancer. To improve the diagnostic accuracy of endoscopy in colorectal lesions, many endoscopic techniques have been applied clinically, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, chromoendoscopy, confocal laser endoscopy, and endocytoscopy (EC). However, with the increasing number of endoscopic resections, the costs associated with the pathological diagnosis of resected specimens have risen year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to differentiate the nature of lesions during colonoscopy.

Endocytoscopy is an ultra-high magnification endoscope that, when combined with chemical staining and narrowband imaging techniques, allows the endoscopist to observe the nuclear morphology of colorectal lesions, the shape of glands, and the morphology of microvessels through the naked eye. This approach avoids the need for pathological examination, achieving the goal of real-time biopsy in vivo. However, the accuracy of endocytoscopic image interpretation requires extensive experience to improve judgment, and there is a certain degree of subjectivity and error in the endoscopist's assessment process. Therefore, to address this issue, clinical applications have proposed using artificial intelligence (AI) for computer-aided diagnosis. The investigator's center has previously developed an AI-assisted diagnostic system based on endocytoscopy with NBI to assist in determining the nature of colorectal lesions. However, forward-looking clinical studies are still lacking to verify the effectiveness of this AI-assisted system. Thus, the investigator aim to conduct such clinical research to validate the clinical efficacy of this AI.

Inclusion Criteria:

• colorectal lesions

Exclusion Criteria:

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

patients with colorectal lesions