This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.
Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.
Voluntarily sign the informed consent form
Promise to abide by the research procedures and cooperate in the implementation of the entire research process.
Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;
Patients who has definite active lower gastrointestinal bleeding.
Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;
Uncontrolled hypertension (systolic blood pressure > 160 mmHg or diastolic blood pressure > 95 mmHg after standardized treatment)
Patients withonic col adenomas or polyps
Case-crossover
Prospective
AI models with NBI
DeviceAI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
The accuracy rate of diagnosing adenomas
The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
The prediction for the disease risk level
The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
Click on the lead sponsor to view all of their trials.
Renmin Hospital of Wuhan University
Lead sponsor
Wuhan University
Sponsor institution
Beijing Friendship Hospital, Captial Medical University
Collaborator
Air Force Military Medical University, China
Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University
Collaborator
Army Medical University, China
Collaborator
Guizhou Provincial People's Hospital
Collaborator
Shengjing Hospital
Collaborator
The Second Medical Center, Chinese PLA General Hospital
Collaborator
Zhejiang University
Collaborator
Shandong University
Collaborator
