Overview

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.

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. In the early stage, our center has developed an AI-assisted diagnostic system based on cellular endoscopy to assist the nature of colorectal lesions, but there is still a lack of prospective clinical study to verify the effectiveness of the AI-assisted system. Therefore, the investigatorr want to carry out this clinical study to verify the clinical effectiveness of the AI.

Eligibility

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 inflammatory polyps, Peutz-Jeghers polyps, juvenile polyps, lymphoma and other pathological types.